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Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

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Awesome Knowledge-Distillation

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Different forms of knowledge

Knowledge from logits

  1. Distilling the knowledge in a neural network. Hinton et al. arXiv:1503.02531
  2. Learning from Noisy Labels with Distillation. Li, Yuncheng et al. ICCV 2017
  3. Training Deep Neural Networks in Generations:A More Tolerant Teacher Educates Better Students. arXiv:1805.05551
  4. Learning Metrics from Teachers: Compact Networks for Image Embedding. Yu, Lu et al. CVPR 2019
  5. Relational Knowledge Distillation. Park, Wonpyo et al. CVPR 2019
  6. On Knowledge Distillation from Complex Networks for Response Prediction. Arora, Siddhartha et al. NAACL 2019
  7. On the Efficacy of Knowledge Distillation. Cho, Jang Hyun & Hariharan, Bharath. arXiv:1910.01348. ICCV 2019
  8. Revisit Knowledge Distillation: a Teacher-free Framework (Revisiting Knowledge Distillation via Label Smoothing Regularization). Yuan, Li et al. CVPR 2020 [code]
  9. Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher. Mirzadeh et al. arXiv:1902.03393
  10. Ensemble Distribution Distillation. ICLR 2020
  11. Noisy Collaboration in Knowledge Distillation. ICLR 2020
  12. On Compressing U-net Using Knowledge Distillation. arXiv:1812.00249
  13. Self-training with Noisy Student improves ImageNet classification. Xie, Qizhe et al.(Google) CVPR 2020
  14. Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. AAAI 2020
  15. Preparing Lessons: Improve Knowledge Distillation with Better Supervision. arXiv:1911.07471
  16. Adaptive Regularization of Labels. arXiv:1908.05474
  17. Positive-Unlabeled Compression on the Cloud. Xu, Yixing et al. (HUAWEI) NeurIPS 2019
  18. Snapshot Distillation: Teacher-Student Optimization in One Generation. Yang, Chenglin et al. CVPR 2019
  19. QUEST: Quantized embedding space for transferring knowledge. Jain, Himalaya et al. arXiv:2020
  20. Conditional teacher-student learning. Z. Meng et al. ICASSP 2019
  21. Subclass Distillation. Müller, Rafael et al. arXiv:2002.03936
  22. MarginDistillation: distillation for margin-based softmax. Svitov, David & Alyamkin, Sergey. arXiv:2003.02586
  23. An Embarrassingly Simple Approach for Knowledge Distillation. Gao, Mengya et al. MLR 2018
  24. Sequence-Level Knowledge Distillation. Kim, Yoon & Rush, Alexander M. arXiv:1606.07947
  25. Boosting Self-Supervised Learning via Knowledge Transfer. Noroozi, Mehdi et al. CVPR 2018
  26. Meta Pseudo Labels. Pham, Hieu et al. ICML 2020 [code]
  27. Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model. CVPR 2020 [code]
  28. Distilled Binary Neural Network for Monaural Speech Separation. Chen Xiuyi et al. IJCNN 2018
  29. Teacher-Class Network: A Neural Network Compression Mechanism. Malik et al. arXiv:2004.03281
  30. Deeply-supervised knowledge synergy. Sun, Dawei et al. CVPR 2019
  31. What it Thinks is Important is Important: Robustness Transfers through Input Gradients. Chan, Alvin et al. CVPR 2020
  32. Triplet Loss for Knowledge Distillation. Oki, Hideki et al. IJCNN 2020
  33. Role-Wise Data Augmentation for Knowledge Distillation. ICLR 2020 [code]
  34. Distilling Spikes: Knowledge Distillation in Spiking Neural Networks. arXiv:2005.00288
  35. Improved Noisy Student Training for Automatic Speech Recognition. Park et al. arXiv:2005.09629
  36. Learning from a Lightweight Teacher for Efficient Knowledge Distillation. Yuang Liu et al. arXiv:2005.09163
  37. ResKD: Residual-Guided Knowledge Distillation. Li, Xuewei et al. arXiv:2006.04719
  38. Distilling Effective Supervision from Severe Label Noise. Zhang, Zizhao. et al. CVPR 2020 [code]
  39. Knowledge Distillation Meets Self-Supervision. Xu, Guodong et al. ECCV 2020 [code]
  40. Self-supervised Knowledge Distillation for Few-shot Learning. arXiv:2006.09785 [code]
  41. Learning with Noisy Class Labels for Instance Segmentation. ECCV 2020
  42. Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation. Wang, Liwei et al. arXiv:2007.01951
  43. Deep Streaming Label Learning. Wang, Zhen et al. ICML 2020 [code]
  44. Teaching with Limited Information on the Learner's Behaviour. Zhang, Yonggang et al. ICML 2020
  45. Discriminability Distillation in Group Representation Learning. Zhang, Manyuan et al. ECCV 2020
  46. Local Correlation Consistency for Knowledge Distillation. ECCV 2020
  47. Prime-Aware Adaptive Distillation. Zhang, Youcai et al. ECCV 2020
  48. One Size Doesn't Fit All: Adaptive Label Smoothing. Krothapalli et al. arXiv:2009.06432
  49. Learning to learn from noisy labeled data. Li, Junnan et al. CVPR 2019
  50. Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization. Wei, Hongxin et al. CVPR 2020
  51. Online Knowledge Distillation via Multi-branch Diversity Enhancement. Li, Zheng et al. ACCV 2020
  52. Pea-KD: Parameter-efficient and Accurate Knowledge Distillation. arXiv:2009.14822
  53. Extending Label Smoothing Regularization with Self-Knowledge Distillation. Wang, Jiyue et al. arXiv:2009.05226
  54. Spherical Knowledge Distillation. Guo, Jia et al. arXiv:2010.07485
  55. Soft-Label Dataset Distillation and Text Dataset Distillation. arXiv:1910.02551
  56. Wasserstein Contrastive Representation Distillation. Chen, Liqun et al. cvpr 2021
  57. Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup. Xu, Guodong et al. cvpr 2021 [code]
  58. Knowledge Refinery: Learning from Decoupled Label. Ding, Qianggang et al. AAAI 2021
  59. Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net. Zhou, Guorui et al. AAAI 2018
  60. Distilling Virtual Examples for Long-tailed Recognition. He, Yin-Yin et al. CVPR 2021

Knowledge from intermediate layers

  1. Fitnets: Hints for thin deep nets. Romero, Adriana et al. arXiv:1412.6550
  2. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. Zagoruyko et al. ICLR 2017
  3. Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks. Zhang, Zhi et al. arXiv:1710.09505
  4. A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning. Yim, Junho et al. CVPR 2017
  5. Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. Huang, Zehao & Wang, Naiyan. 2017
  6. Paraphrasing complex network: Network compression via factor transfer. Kim, Jangho et al. NeurIPS 2018
  7. Knowledge transfer with jacobian matching. ICML 2018
  8. Self-supervised knowledge distillation using singular value decomposition. Lee, Seung Hyun et al. ECCV 2018
  9. Learning Deep Representations with Probabilistic Knowledge Transfer. Passalis et al. ECCV 2018
  10. Variational Information Distillation for Knowledge Transfer. Ahn, Sungsoo et al. CVPR 2019
  11. Knowledge Distillation via Instance Relationship Graph. Liu, Yufan et al. CVPR 2019
  12. Knowledge Distillation via Route Constrained Optimization. Jin, Xiao et al. ICCV 2019
  13. Similarity-Preserving Knowledge Distillation. Tung, Frederick, and Mori Greg. ICCV 2019
  14. MEAL: Multi-Model Ensemble via Adversarial Learning. Shen,Zhiqiang, He,Zhankui, and Xue Xiangyang. AAAI 2019
  15. A Comprehensive Overhaul of Feature Distillation. Heo, Byeongho et al. ICCV 2019 [code]
  16. Feature-map-level Online Adversarial Knowledge Distillation. ICML 2020
  17. Distilling Object Detectors with Fine-grained Feature Imitation. ICLR 2020
  18. Knowledge Squeezed Adversarial Network Compression. Changyong, Shu et al. AAAI 2020
  19. Stagewise Knowledge Distillation. Kulkarni, Akshay et al. arXiv: 1911.06786
  20. Knowledge Distillation from Internal Representations. AAAI 2020
  21. Knowledge Flow:Improve Upon Your Teachers. ICLR 2019
  22. LIT: Learned Intermediate Representation Training for Model Compression. ICML 2019
  23. Improving the Adversarial Robustness of Transfer Learning via Noisy Feature Distillation. Chin, Ting-wu et al. arXiv:2002.02998
  24. Knapsack Pruning with Inner Distillation. Aflalo, Yonathan et al. arXiv:2002.08258
  25. Residual Knowledge Distillation. Gao, Mengya et al. arXiv:2002.09168
  26. Knowledge distillation via adaptive instance normalization. Yang, Jing et al. arXiv:2003.04289
  27. Bert-of-Theseus: Compressing bert by progressive module replacing. Xu, Canwen et al. arXiv:2002.02925 [code]
  28. Distilling Spikes: Knowledge Distillation in Spiking Neural Networks. arXiv:2005.00727
  29. Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks. Meet et al. arXiv:2005.08110
  30. Feature-map-level Online Adversarial Knowledge Distillation. Chung, Inseop et al. ICML 2020
  31. Channel Distillation: Channel-Wise Attention for Knowledge Distillation. Zhou, Zaida et al. arXiv:2006.01683 [code]
  32. Matching Guided Distillation. ECCV 2020 [code]
  33. Differentiable Feature Aggregation Search for Knowledge Distillation. ECCV 2020
  34. Interactive Knowledge Distillation. Fu, Shipeng et al. arXiv:2007.01476
  35. Feature Normalized Knowledge Distillation for Image Classification. ECCV 2020 [code]
  36. Layer-Level Knowledge Distillation for Deep Neural Networks. Li, Hao Ting et al. Applied Sciences, 2019
  37. Knowledge Distillation with Feature Maps for Image Classification. Chen, Weichun et al. ACCV 2018
  38. Efficient Kernel Transfer in Knowledge Distillation. Qian, Qi et al. arXiv:2009.14416
  39. Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition. arXiv:2009.06902
  40. Kernel Based Progressive Distillation for Adder Neural Networks. Xu, Yixing et al. NeurIPS 2020
  41. Feature Distillation With Guided Adversarial Contrastive Learning. Bai, Tao et al. arXiv:2009.09922
  42. Pay Attention to Features, Transfer Learn Faster CNNs. Wang, Kafeng et al. ICLR 2019
  43. Multi-level Knowledge Distillation. Ding, Fei et al. arXiv:2012.00573
  44. Cross-Layer Distillation with Semantic Calibration. Chen, Defang et al. AAAI 2021 [code]
  45. Harmonized Dense Knowledge Distillation Training for Multi-­Exit Architectures. Wang, Xinglu & Li, Yingming. AAAI 2021
  46. Robust Knowledge Transfer via Hybrid Forward on the Teacher-Student Model. Song, Liangchen et al. AAAI 2021
  47. Show, Attend and Distill: Knowledge Distillation via Attention-­Based Feature Matching. Ji, Mingi et al. AAAI 2021
  48. MINILMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers. Wang, Wenhui et al. arXiv:2012.15828
  49. ALP-KD: Attention-Based Layer Projection for Knowledge Distillation. Peyman et al. AAAI 2021
  50. PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation. Reyhan et al. arXiv:2103.00053
  51. Fixing the Teacher-Student Knowledge Discrepancy in Distillation. Han, Jiangfan et al. arXiv:2103.16844

Graph-based

  1. Graph-based Knowledge Distillation by Multi-head Attention Network. Lee, Seunghyun and Song, Byung. Cheol arXiv:1907.02226
  2. Graph Representation Learning via Multi-task Knowledge Distillation. arXiv:1911.05700
  3. Deep geometric knowledge distillation with graphs. arXiv:1911.03080
  4. Better and faster: Knowledge transfer from multiple self-supervised learning tasks via graph distillation for video classification. IJCAI 2018
  5. Distillating Knowledge from Graph Convolutional Networks. Yang, Yiding et al. CVPR 2020 [code]
  6. Saliency Prediction with External Knowledge. Zhang, Yifeng et al. arXiv:2007.13839
  7. Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge. Huang, He et al. arXiv:2007.15610
  8. Reliable Data Distillation on Graph Convolutional Network. Zhang, Wentao et al. ACM SIGMOD 2020
  9. Mutual Teaching for Graph Convolutional Networks. Zhan, Kun et al. Future Generation Computer Systems, 2021
  10. DistilE: Distiling Knowledge Graph Embeddings for Faster and Cheaper Reasoning. Zhu, Yushan et al. arXiv:2009.05912
  11. Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation. Antaris, Stefanos & Rafailidis, Dimitrios. arXiv:2011.05664
  12. On Self-Distilling Graph Neural Network. Chen, Yuzhao et al. arXiv:2011.02255
  13. Iterative Graph Self Distillation. iclr 2021
  14. Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework. Yang, Cheng et al. WWW 2021 [code]

Mutual Information & Online Learning

  1. Correlation Congruence for Knowledge Distillation. Peng, Baoyun et al. ICCV 2019
  2. Similarity-Preserving Knowledge Distillation. Tung, Frederick, and Mori Greg. ICCV 2019
  3. Variational Information Distillation for Knowledge Transfer. Ahn, Sungsoo et al. CVPR 2019
  4. Contrastive Representation Distillation. Tian, Yonglong et al. ICLR 2020 [RepDistill]
  5. Online Knowledge Distillation via Collaborative Learning. Guo, Qiushan et al. CVPR 2020
  6. Peer Collaborative Learning for Online Knowledge Distillation. Wu, Guile & Gong, Shaogang. AAAI 2021
  7. Knowledge Transfer via Dense Cross-layer Mutual-distillation. ECCV 2020
  8. MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution. Yang, Taojiannan et al. ECCV 2020 [code]
  9. AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation. ECCV 2020
  10. Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge. Li, Kang et al. AAAI 2021
  11. *Federated Knowledge Distillation. Seo, Hyowoon et al. arXiv:2011.02367
  12. Unsupervised Image Segmentation using Mutual Mean-Teaching. Wu, Zhichao et al.arXiv:2012.08922
  13. Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning. Cai, Zhaowei et al. arXiv:2101.08482

Self-KD

  1. Moonshine:Distilling with Cheap Convolutions. Crowley, Elliot J. et al. NeurIPS 2018
  2. Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. Zhang, Linfeng et al. ICCV 2019
  3. Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Hou, Yuenan et al. ICCV 2019
  4. BAM! Born-Again Multi-Task Networks for Natural Language Understanding. Clark, Kevin et al. ACL 2019,short
  5. Self-Knowledge Distillation in Natural Language Processing. Hahn, Sangchul and Choi, Heeyoul. arXiv:1908.01851
  6. Rethinking Data Augmentation: Self-Supervision and Self-Distillation. Lee, Hankook et al. ICLR 2020
  7. MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks. arXiv:1911.09418
  8. Self-Distillation Amplifies Regularization in Hilbert Space. Mobahi, Hossein et al. NeurIPS 2020
  9. MINILM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers. Wang, Wenhui et al. arXiv:2002.10957
  10. Regularizing Class-wise Predictions via Self-knowledge Distillation. CVPR 2020 [code]
  11. Self-Distillation as Instance-Specific Label Smoothing. Zhang, Zhilu & Sabuncu, Mert R. NeurIPS 2020
  12. Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training. Chen, Xuxi et al. ICML 2020 [code]
  13. S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. arXiv:2009.08348
  14. Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection. Huang, Zeyi et al. NeurIPS 2020
  15. Distillation-Based Training for Multi-Exit Architectures. Phuong, Mary and Lampert, Christoph H. ICCV 2019
  16. Pair-based self-distillation for semi-supervised domain adaptation. iclr 2021
  17. SEED: SElf-SupErvised Distillation. ICLR 2021
  18. Self-Feature Regularization: Self-Feature Distillation Without Teacher Models. Fan, Wenxuan & Hou, Zhenyan.arXiv:2103.07350
  19. Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation. Ji, Mingi et al. CVPR 2021 [code]

Structural Knowledge

  1. Paraphrasing Complex Network:Network Compression via Factor Transfer. Kim, Jangho et al. NeurIPS 2018
  2. Relational Knowledge Distillation. Park, Wonpyo et al. CVPR 2019
  3. Knowledge Distillation via Instance Relationship Graph. Liu, Yufan et al. CVPR 2019
  4. Contrastive Representation Distillation. Tian, Yonglong et al. ICLR 2020
  5. Teaching To Teach By Structured Dark Knowledge. ICLR 2020
  6. Inter-Region Affinity Distillation for Road Marking Segmentation. Hou, Yuenan et al. CVPR 2020 [code]
  7. Heterogeneous Knowledge Distillation using Information Flow Modeling. Passalis et al. CVPR 2020 [code]
  8. Asymmetric metric learning for knowledge transfer. Budnik, Mateusz & Avrithis, Yannis. arXiv:2006.16331
  9. Local Correlation Consistency for Knowledge Distillation. ECCV 2020
  10. Few-Shot Class-Incremental Learning. Tao, Xiaoyu et al. CVPR 2020
  11. Semantic Relation Preserving Knowledge Distillation for Image-to-Image Translation. ECCV 2020
  12. Interpretable Foreground Object Search As Knowledge Distillation. ECCV 2020
  13. Improving Knowledge Distillation via Category Structure. ECCV 2020
  14. Few-Shot Class-Incremental Learning via Relation Knowledge Distillation. Dong, Songlin et al. AAAI 2021

Privileged Information

  1. Learning using privileged information: similarity control and knowledge transfer. Vapnik, Vladimir and Rauf, Izmailov. MLR 2015
  2. Unifying distillation and privileged information. Lopez-Paz, David et al. ICLR 2016
  3. Model compression via distillation and quantization. Polino, Antonio et al. ICLR 2018
  4. KDGAN:Knowledge Distillation with Generative Adversarial Networks. Wang, Xiaojie. NeurIPS 2018
  5. Efficient Video Classification Using Fewer Frames. Bhardwaj, Shweta et al. CVPR 2019
  6. Retaining privileged information for multi-task learning. Tang, Fengyi et al. KDD 2019
  7. A Generalized Meta-loss function for regression and classification using privileged information. Asif, Amina et al. arXiv:1811.06885
  8. Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks. Gao, Di & Zhuo, Cheng. AAAI 2020
  9. Privileged Knowledge Distillation for Online Action Detection. Zhao, Peisen et al. cvpr 2021
  10. Adversarial Distillation for Learning with Privileged Provisions. Wang, Xiaojie et al. TPAMI 2019

KD + GAN

  1. Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. Xu, Zheng et al. arXiv:1709.00513
  2. KTAN: Knowledge Transfer Adversarial Network. Liu, Peiye et al. arXiv:1810.08126
  3. KDGAN:Knowledge Distillation with Generative Adversarial Networks. Wang, Xiaojie. NeurIPS 2018
  4. Adversarial Learning of Portable Student Networks. Wang, Yunhe et al. AAAI 2018
  5. Adversarial Network Compression. Belagiannis, Vasileios et al. ECCV 2018
  6. Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks. ICASSP 2018
  7. Adversarial Distillation for Efficient Recommendation with External Knowledge. TOIS 2018
  8. Training student networks for acceleration with conditional adversarial networks. Xu, Zheng et al. BMVC 2018
  9. DAFL:Data-Free Learning of Student Networks. Chen, Hanting et al. ICCV 2019
  10. MEAL: Multi-Model Ensemble via Adversarial Learning. Shen,Zhiqiang, He,Zhankui, and Xue Xiangyang. AAAI 2019
  11. Knowledge Distillation with Adversarial Samples Supporting Decision Boundary. Heo, Byeongho et al. AAAI 2019
  12. Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection. Liu, Jian et al. AAAI 2019
  13. Adversarially Robust Distillation. Goldblum, Micah et al. AAAI 2020
  14. GAN-Knowledge Distillation for one-stage Object Detection. Hong, Wei et al. arXiv:1906.08467
  15. Lifelong GAN: Continual Learning for Conditional Image Generation. Kundu et al. arXiv:1908.03884
  16. Compressing GANs using Knowledge Distillation. Aguinaldo, Angeline et al. arXiv:1902.00159
  17. Feature-map-level Online Adversarial Knowledge Distillation. ICML 2020
  18. MineGAN: effective knowledge transfer from GANs to target domains with few images. Wang, Yaxing et al. CVPR 2020
  19. Distilling portable Generative Adversarial Networks for Image Translation. Chen, Hanting et al. AAAI 2020
  20. GAN Compression: Efficient Architectures for Interactive Conditional GANs. Junyan Zhu et al. CVPR 2020 [code]
  21. Adversarial network compression. Belagiannis et al. ECCV 2018
  22. P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection. Zhang, Zhiwei et al. IJCAI 2020
  23. StyleGAN2 Distillation for Feed-forward Image Manipulation. Viazovetskyi et al. ECCV 2020 [code]
  24. HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing. ECCV 2020
  25. TinyGAN: Distilling BigGAN for Conditional Image Generation. ACCV 2020 [code]
  26. Learning Efficient GANs via Differentiable Masks and co-Attention Distillation. Li, Shaojie et al. aaai 2021 [code]
  27. Self-Supervised GAN Compression. Yu, Chong & Pool, Jeff. arXiv:2007.01491
  28. Teachers Do More Than Teach: Compressing Image-to-Image Models. CVPR 2021 [code]

KD + Meta-learning

  1. Few Sample Knowledge Distillation for Efficient Network Compression. Li, Tianhong et al. CVPR 2020
  2. Learning What and Where to Transfer. Jang, Yunhun et al, ICML 2019
  3. Transferring Knowledge across Learning Processes. Moreno, Pablo G et al. ICLR 2019
  4. Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval. Liu, Qing et al. ICCV 2019
  5. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. Dvornik, Nikita et al. ICCV 2019
  6. Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation. arXiv:1911.05329v1
  7. Progressive Knowledge Distillation For Generative Modeling. ICLR 2020
  8. Few Shot Network Compression via Cross Distillation. AAAI 2020
  9. MetaDistiller: Network Self-boosting via Meta-learned Top-down Distillation. Liu, Benlin et al. ECCV 2020
  10. Few-Shot Learning with Intra-Class Knowledge Transfer. arXiv:2008.09892
  11. Few-Shot Object Detection via Knowledge Transfer. Kim, Geonuk et al. arXiv:2008.12496
  12. Distilled One-Shot Federated Learning. arXiv:2009.07999
  13. Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains. Pan, Haojie et al. arXiv:2012.01266
  14. Progressive Network Grafting for Few-Shot Knowledge Distillation. Shen, Chengchao et al. AAAI 2021

Data-free KD

  1. Data-Free Knowledge Distillation for Deep Neural Networks. NeurIPS 2017
  2. Zero-Shot Knowledge Distillation in Deep Networks. ICML 2019
  3. DAFL:Data-Free Learning of Student Networks. ICCV 2019
  4. Zero-shot Knowledge Transfer via Adversarial Belief Matching. Micaelli, Paul and Storkey, Amos. NeurIPS 2019
  5. Dream Distillation: A Data-Independent Model Compression Framework. Kartikeya et al. ICML 2019
  6. Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion. Yin, Hongxu et al. CVPR 2020 [code]
  7. Data-Free Adversarial Distillation. Fang, Gongfan et al. CVPR 2020
  8. The Knowledge Within: Methods for Data-Free Model Compression. Haroush, Matan et al. CVPR 2020
  9. Knowledge Extraction with No Observable Data. Yoo, Jaemin et al. NeurIPS 2019 [code]
  10. Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN. CVPR 2020
  11. DeGAN: Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier. Addepalli, Sravanti et al. arXiv:1912.11960
  12. Generative Low-bitwidth Data Free Quantization. Xu, Shoukai et al. ECCV 2020 [code]
  13. This dataset does not exist: training models from generated images. arXiv:1911.02888
  14. MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation. Sanjay et al. arXiv:2005.03161
  15. Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. Such et al. ECCV 2020
  16. Billion-scale semi-supervised learning for image classification. FAIR. arXiv:1905.00546 [code]
  17. Data-Free Network Quantization With Adversarial Knowledge Distillation. Choi, Yoojin et al. CVPRW 2020
  18. Adversarial Self-Supervised Data-Free Distillation for Text Classification. EMNLP 2020
  19. Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer. arXiv:2010.07334
  20. Data-free Knowledge Distillation for Segmentation using Data-Enriching GAN. Bhogale et al. arXiv:2011.00809
  21. Layer-Wise Data-Free CNN Compression. Horton, Maxwell et al (Apple Inc.). cvpr 2021
  22. Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation. Nayak et al. WACV 2021
  23. Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization. Li, Yuhang et al. cvpr 2021
  24. Large-Scale Generative Data-Free Distillation. Luo, Liangchen et al. cvpr 2021
  25. Domain Impression: A Source Data Free Domain Adaptation Method. Kurmi et al. WACV 2021
  26. Learning Student Networks in the Wild. (HUAWEI-Noah). CVPR 2021
  27. Data-Free Knowledge Distillation For Image Super-Resolution. (HUAWEI-Noah). CVPR 2021
  28. Zero-shot Adversarial Quantization. Liu, Yuang et al. CVPR 2021 [code]
  29. Source-Free Domain Adaptation for Semantic Segmentation. Liu, Yuang et al. CVPR 2021
  30. Data-Free Model Extraction. Jean-Baptiste et al. CVPR 2021 [code]
  31. Delving into Data: Effectively Substitute Training for Black-box Attack. CVPR 2021
  32. Zero-Shot Knowledge Distillation Using Label-Free Adversarial Perturbation With Taylor Approximation. Li, Kang et al. IEEE Access, 2021.

other data-free model compression:

  • Data-free Parameter Pruning for Deep Neural Networks. Srinivas, Suraj et al. arXiv:1507.06149
  • Data-Free Quantization Through Weight Equalization and Bias Correction. Nagel, Markus et al. ICCV 2019
  • DAC: Data-free Automatic Acceleration of Convolutional Networks. Li, Xin et al. WACV 2019
  • A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework. Zhan, Zheng et al. arXiv:2003.06513
  • ZeroQ: A Novel Zero Shot Quantization Framework. Cai et al. CVPR 2020 [code]
  • Diversifying Sample Generation for Data-Free Quantization. Zhang, Xiangguo et al. CVPR 2021

KD + AutoML

  1. Improving Neural Architecture Search Image Classifiers via Ensemble Learning. Macko, Vladimir et al. arXiv:1903.06236
  2. Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Li, Changlin et al. CVPR 2020
  3. Towards Oracle Knowledge Distillation with Neural Architecture Search. Kang, Minsoo et al. AAAI 2020
  4. Search for Better Students to Learn Distilled Knowledge. Gu, Jindong & Tresp, Volker arXiv:2001.11612
  5. Circumventing Outliers of AutoAugment with Knowledge Distillation. Wei, Longhui et al. arXiv:2003.11342
  6. Network Pruning via Transformable Architecture Search. Dong, Xuanyi & Yang, Yi. NeurIPS 2019
  7. Search to Distill: Pearls are Everywhere but not the Eyes. Liu Yu et al. CVPR 2020
  8. AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks. Fu, Yonggan et al. ICML 2020 [code]
  9. Joint-DetNAS: Upgrade Your Detector with NAS,Pruning and Dynamic Distillation. CVPR 2021

KD + RL

  1. N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. Ashok, Anubhav et al. ICLR 2018
  2. Knowledge Flow:Improve Upon Your Teachers. Liu, Iou-jen et al. ICLR 2019
  3. Transferring Knowledge across Learning Processes. Moreno, Pablo G et al. ICLR 2019
  4. Exploration by random network distillation. Burda, Yuri et al. ICLR 2019
  5. Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. Hong, Zhang-Wei et al. arXiv:2002.00149
  6. Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach. Xue, Zeyue et al. arXiv:2002.02202
  7. Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. Cha, han et al. arXiv:2005.06105
  8. Dual Policy Distillation. Lai, Kwei-Herng et al. IJCAI 2020
  9. Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location. El-Bouri, Rasheed et al. ICML 2020
  10. Reinforced Multi-Teacher Selection for Knowledge Distillation. Yuan, Fei et al. AAAI 2021
  11. Universal Trading for Order Execution with Oracle Policy Distillation. Fang, Yuchen et al. AAAI 2021
  12. Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation. Dunnhofer et al. IEEE RAL

KD + Self-supervised

  1. Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation. ECCV 2020
  2. Self-supervised Label Augmentation via Input Transformations. Lee, Hankook et al. ICML 2020 [code]
  3. Improving Object Detection with Selective Self-supervised Self-training. Li, Yandong et al. ECCV 2020
  4. Distilling Visual Priors from Self-Supervised Learning. Zhao, Bingchen & Wen, Xin. ECCVW 2020
  5. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. Grill et al. arXiv:2006.07733 [code]
  6. Unpaired Learning of Deep Image Denoising. Wu, Xiaohe et al. arXiv:2008.13711 [code]
  7. SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification. Yin, Junhui et al. arXiv:2009.05972
  8. Introspective Learning by Distilling Knowledge from Online Self-explanation. Gu, Jindong et al. ACCV 2020
  9. Robust Pre-Training by Adversarial Contrastive Learning. Jiang, Ziyu et al. NeurIPS 2020 [code]
  10. CompRess: Self-Supervised Learning by Compressing Representations. Koohpayegani et al. NeurIPS 2020 [code]
  11. Big Self-Supervised Models are Strong Semi-Supervised Learners. Che, Ting et al. NeurIPS 2020 [code]
  12. Rethinking Pre-training and Self-training. Zoph, Barret et al. NeurIPS 2020 [code]
  13. ISD: Self-Supervised Learning by Iterative Similarity Distillation. Tejankar et al. cvpr 2021 [code]
  14. Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning. Li, Zeming et al. arXiv:2101.07525
  15. Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones. Cui, Cheng et al. arXiv:2103.05959

Multi-teacher and Ensemble KD

  1. Learning from Multiple Teacher Networks. You, Shan et al. KDD 2017
  2. Learning with single-teacher multi-student. You, Shan et al. AAAI 2018
  3. Knowledge distillation by on-the-fly native ensemble. Lan, Xu et al. NeurIPS 2018
  4. Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017
  5. Knowledge Adaptation: Teaching to Adapt. Arxiv:1702.02052
  6. Deep Model Compression: Distilling Knowledge from Noisy Teachers. Sau, Bharat Bhusan et al. arXiv:1610.09650
  7. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Tarvainen, Antti and Valpola, Harri. NeurIPS 2017
  8. Born-Again Neural Networks. Furlanello, Tommaso et al. ICML 2018
  9. Deep Mutual Learning. Zhang, Ying et al. CVPR 2018
  10. Collaborative learning for deep neural networks. Song, Guocong and Chai, Wei. NeurIPS 2018
  11. Data Distillation: Towards Omni-Supervised Learning. Radosavovic, Ilija et al. CVPR 2018
  12. Multilingual Neural Machine Translation with Knowledge Distillation. ICLR 2019
  13. Unifying Heterogeneous Classifiers with Distillation. Vongkulbhisal et al. CVPR 2019
  14. Distilled Person Re-Identification: Towards a More Scalable System. Wu, Ancong et al. CVPR 2019
  15. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. Dvornik, Nikita et al. ICCV 2019
  16. Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System. Yang, Ze et al. WSDM 2020
  17. FEED: Feature-level Ensemble for Knowledge Distillation. Park, SeongUk and Kwak, Nojun. AAAI 2020
  18. Stochasticity and Skip Connection Improve Knowledge Transfer. Lee, Kwangjin et al. ICLR 2020
  19. Online Knowledge Distillation with Diverse Peers. Chen, Defang et al. AAAI 2020
  20. Hydra: Preserving Ensemble Diversity for Model Distillation. Tran, Linh et al. arXiv:2001.04694
  21. Distilled Hierarchical Neural Ensembles with Adaptive Inference Cost. Ruiz, Adria et al. arXv:2003.01474
  22. Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition. Gao, Yan et al. arXiv:2005.09310
  23. Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery. ECCV 2020
  24. Collaborative Learning for Faster StyleGAN Embedding. Guan, Shanyan et al. arXiv:2007.01758
  25. Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection. Chen, Cong et al. IEEE 2020 [code]
  26. Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation. MICCAI 2020
  27. Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation. Nguyen-Meidine et al. WACV 2020
  28. Semi-supervised Learning with Teacher-student Network for Generalized Attribute Prediction. Shin, Minchul et al. ECCV 2020
  29. Knowledge Distillation for Multi-task Learning. Li, WeiHong & Bilen, Hakan. arXiv:2007.06889 [project]
  30. Adaptive Multi-Teacher Multi-level Knowledge Distillation. Liu, Yuang et al. Neurocomputing 2020 [code]
  31. Online Ensemble Model Compression using Knowledge Distillation. ECCV 2020
  32. Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification. ECCV 2020
  33. Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge. He, Chaoyang et al. arXiv:2007.14513
  34. Densely Guided Knowledge Distillation using Multiple Teacher Assistants. Son, Wonchul et l. arXiv:2009.08825
  35. ProxylessKD: Direct Knowledge Distillation with Inherited Classifier for Face Recognition. Shi, Weidong et al. arXiv:2011.00265
  36. Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space. Du, Shangchen et al. NeurIPS 2020 [code]
  37. Reinforced Multi‐Teacher Selection for Knowledge Distillation. Yuan, Fei et al. AAAI 2021
  38. Class-­Incremental Instance Segmentation via Multi­‐Teacher Networks. Gu, Yanan et al. AAAI 2021
  39. Collaborative Teacher-Student Learning via Multiple Knowledge Transfer. Sun, Liyuan et al. arXiv:2101.08471
  40. Efficient Conditional GAN Transfer with Knowledge Propagation across Classes. Shahbaziet al. CVPR 2021 [code]
  41. Knowledge Evolution in Neural Networks. Taha, Ahmed et al. CVPR 2021 [code]
  42. Distilling a Powerful Student Model via Online Knowledge Distillation. Li, Shaojie et al. arXiv:2103.14473

Knowledge Amalgamation(KA) - zju-VIPA

VIPA - KA

  1. Amalgamating Knowledge towards Comprehensive Classification. Shen, Chengchao et al. AAAI 2019
  2. Amalgamating Filtered Knowledge : Learning Task-customized Student from Multi-task Teachers. Ye, Jingwen et al. IJCAI 2019
  3. Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning. Luo, Sihui et al. IJCAI 2019
  4. Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More. Ye, Jingwen et al. CVPR 2019
  5. Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation. ICCV 2019
  6. Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN. CVPR 2020

Cross-modal / DA / Incremental Learning

  1. SoundNet: Learning Sound Representations from Unlabeled Video SoundNet Architecture. Aytar, Yusuf et al. NeurIPS 2016
  2. Cross Modal Distillation for Supervision Transfer. Gupta, Saurabh et al. CVPR 2016
  3. Emotion recognition in speech using cross-modal transfer in the wild. Albanie, Samuel et al. ACM MM 2018
  4. Through-Wall Human Pose Estimation Using Radio Signals. Zhao, Mingmin et al. CVPR 2018
  5. Compact Trilinear Interaction for Visual Question Answering. Do, Tuong et al. ICCV 2019
  6. Cross-Modal Knowledge Distillation for Action Recognition. Thoker, Fida Mohammad and Gall, Juerge. ICIP 2019
  7. Learning to Map Nearly Anything. Salem, Tawfiq et al. arXiv:1909.06928
  8. Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval. Liu, Qing et al. ICCV 2019
  9. UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation. Kundu et al. ICCV 2019
  10. CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency. Chen, Yun-Chun et al. CVPR 2019
  11. XD:Cross lingual Knowledge Distillation for Polyglot Sentence Embeddings. ICLR 2020
  12. Effective Domain Knowledge Transfer with Soft Fine-tuning. Zhao, Zhichen et al. arXiv:1909.02236
  13. ASR is all you need: cross-modal distillation for lip reading. Afouras et al. arXiv:1911.12747v1
  14. Knowledge distillation for semi-supervised domain adaptation. arXiv:1908.07355
  15. Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition. Meng, Zhong et al. arXiv:2001.01798
  16. Cluster Alignment with a Teacher for Unsupervised Domain Adaptation. ICCV 2019
  17. Attention Bridging Network for Knowledge Transfer. Li, Kunpeng et al. ICCV 2019
  18. Unpaired Multi-modal Segmentation via Knowledge Distillation. Dou, Qi et al. arXiv:2001.03111
  19. Multi-source Distilling Domain Adaptation. Zhao, Sicheng et al. arXiv:1911.11554
  20. Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing. Hu, Hengtong et al. CVPR 2020
  21. Improving Semantic Segmentation via Self-Training. Zhu, Yi et al. arXiv:2004.14960
  22. Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation. arXiv:2005.08213
  23. Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation. arXiv:2005.07839
  24. Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge. Zhao, Long et al. CVPR 2020
  25. Large-Scale Domain Adaptation via Teacher-Student Learning. Li, Jinyu et al. arXiv:1708.05466
  26. Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data. Fayek, Haytham M. & Kumar, Anurag. IJCAI 2020
  27. Distilling Cross-Task Knowledge via Relationship Matching. Ye, Han-Jia. et al. CVPR 2020 [code]
  28. Modality distillation with multiple stream networks for action recognition. Garcia, Nuno C. et al. ECCV 2018
  29. Domain Adaptation through Task Distillation. Zhou, Brady et al. ECCV 2020 [code]
  30. Dual Super-Resolution Learning for Semantic Segmentation. Wang, Li et al. CVPR 2020 [code]
  31. Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation. Jing, Taotao et al. ACM MM 2020
  32. Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation. Peng, Xingchao et al. ECCV 2020 [code]
  33. Unsupervised Domain Adaptive Knowledge Distillation for Semantic Segmentation. Kothandaraman et al. arXiv:2011.08007
  34. A Student‐Teacher Architecture for Dialog Domain Adaptation under the Meta‐Learning Setting. Qian, Kun et al. AAAI 2021
  35. Multimodal Fusion via Teacher-­‐Student Network for Indoor Action Recognition. Bruce et al. AAAI 2021
  36. Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation. Li, Kang et al. TMI 2021
  37. Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains. Nguyen et al. IVC 2021
  38. Feature-Supervised Action Modality Transfer. Thoker, Fida Mohammad and Snoek, Cees. ICPR 2020.
  39. There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge. Francisco et al. CVPR 2021
  40. Adaptive Consistency Regularization for Semi-Supervised Transfer Learning Abulikemu. Abulikemu et al. CVPR 2021 [code]
  41. Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning. Cheraghian et al. CVPR 2021
  42. Distilling Causal Effect of Data in Class-Incremental Learning. Hu, Xinting et al. CVPR 2021 [code]
  43. Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation. Chen, Shuaijun et al. CVPR 2021
  44. PLOP: Learning without Forgetting for Continual Semantic Segmentation. Arthur et al. CVPR 2021
  45. Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations. Umberto & Pietro. CVPR 2021
  46. Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution. Sun, Baoli et al. CVPR 2021 [code]
  47. CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning. Wei, Chen et al. CVPR 2021
  48. Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation. Zheng, Zhedong & Yang, Yi. CVPR 2021
  49. Image Classification in the Dark Using Quanta Image Sensors. Gnanasambandam, Abhiram & Chan, Stanley H. ECCV 2020
  50. Dynamic Low-Light Imaging with Quanta Image Sensors. Chi, Yiheng et al. ECCV 2020

Application of KD

  1. Face model compression by distilling knowledge from neurons. Luo, Ping et al. AAAI 2016
  2. Learning efficient object detection models with knowledge distillation. Chen, Guobin et al. NeurIPS 2017
  3. Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy. Mishra, Asit et al. NeurIPS 2018
  4. Distilled Person Re-identification: Towars a More Scalable System. Wu, Ancong et al. CVPR 2019
  5. Efficient Video Classification Using Fewer Frames. Bhardwaj, Shweta et al. CVPR 2019
  6. Fast Human Pose Estimation. Zhang, Feng et al. CVPR 2019
  7. Distilling knowledge from a deep pose regressor network. Saputra et al. arXiv:1908.00858 (2019)
  8. Learning Lightweight Lane Detection CNNs by Self Attention Distillation. Hou, Yuenan et al. ICCV 2019
  9. Structured Knowledge Distillation for Semantic Segmentation. Liu, Yifan et al. CVPR 2019
  10. Relation Distillation Networks for Video Object Detection. Deng, Jiajun et al. ICCV 2019
  11. Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection. Dong, Xuanyi and Yang, Yi. ICCV 2019
  12. Progressive Teacher-student Learning for Early Action Prediction. Wang, Xionghui et al. CVPR 2019
  13. Lightweight Image Super-Resolution with Information Multi-distillation Network. Hui, Zheng et al. ICCVW 2019
  14. AWSD:Adaptive Weighted Spatiotemporal Distillation for Video Representation. Tavakolian, Mohammad et al. ICCV 2019
  15. Dynamic Kernel Distillation for Efficient Pose Estimation in Videos. Nie, Xuecheng et al. ICCV 2019
  16. Teacher Guided Architecture Search. Bashivan, Pouya and Tensen, Mark. ICCV 2019
  17. Online Model Distillation for Efficient Video Inference. Mullapudi et al. ICCV 2019
  18. Distilling Object Detectors with Fine-grained Feature Imitation. Wang, Tao et al. CVPR 2019
  19. Relation Distillation Networks for Video Object Detection. Deng, Jiajun et al. ICCV 2019
  20. Knowledge Distillation for Incremental Learning in Semantic Segmentation. arXiv:1911.03462
  21. MOD: A Deep Mixture Model with Online Knowledge Distillation for Large Scale Video Temporal Concept Localization. arXiv:1910.12295
  22. Teacher-Students Knowledge Distillation for Siamese Trackers. arXiv:1907.10586
  23. LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning. Zhao, Albert et al. CVPR 2020(pre)
  24. Knowledge Distillation for Brain Tumor Segmentation. arXiv:2002.03688
  25. ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes. Chen, Yuhua et al. CVPR 2018
  26. Multi-Representation Knowledge Distillation For Audio Classification. Gao, Liang et al. arXiv:2002.09607
  27. Collaborative Distillation for Ultra-Resolution Universal Style Transfer. Wang, Huan et al. CVPR 2020 [code]
  28. ShadowTutor: Distributed Partial Distillation for Mobile Video DNN Inference. Chung, Jae-Won et al. ICPP 2020 [code]
  29. Object Relational Graph with Teacher-Recommended Learning for Video Captioning. Zhang, Ziqi et al. CVPR 2020
  30. Spatio-Temporal Graph for Video Captioning with Knowledge distillation. CVPR 2020 [code]
  31. Squeezed Deep 6DoF Object Detection Using Knowledge Distillation. Felix, Heitor et al. arXiv:2003.13586
  32. Distilled Semantics for Comprehensive Scene Understanding from Videos. Tosi, Fabio et al. arXiv:2003.14030
  33. Parallel WaveNet: Fast high-fidelity speech synthesis. Van et al. ICML 2018
  34. Distill Knowledge From NRSfM for Weakly Supervised 3D Pose Learning. Wang Chaoyang et al. ICCV 2019
  35. KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow. Murugesan et al. MIDL 2020
  36. Geometry-Aware Distillation for Indoor Semantic Segmentation. Jiao, Jianbo et al. CVPR 2019
  37. Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection. ICCV 2019
  38. Distill Image Dehazing with Heterogeneous Task Imitation. Hong, Ming et al. CVPR 2020
  39. Knowledge Distillation for Action Anticipation via Label Smoothing. Camporese et al. arXiv:2004.07711
  40. More Grounded Image Captioning by Distilling Image-Text Matching Model. Zhou, Yuanen et al. CVPR 2020
  41. Distilling Knowledge from Refinement in Multiple Instance Detection Networks. Zeni, Luis Felipe & Jung, Claudio. arXiv:2004.10943
  42. Enabling Incremental Knowledge Transfer for Object Detection at the Edge. arXiv:2004.05746
  43. Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings. Bergmann, Paul et al. CVPR 2020
  44. TA-Student VQA: Multi-Agents Training by Self-Questioning. Xiong, Peixi & Wu Ying. CVPR 2020
  45. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. Jiang, Lu et al. ICML 2018
  46. A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection. Chen, Zhihao et al. CVPR 2020 [code]
  47. Learning Lightweight Face Detector with Knowledge Distillation. Zhang Shifeng et al. IEEE 2019
  48. Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation. ICIP 2019
  49. Distilling Object Detectors with Task Adaptive Regularization. Sun, Ruoyu et al. arXiv:2006.13108
  50. Intra-class Compactness Distillation for Semantic Segmentation. ECCV 2020
  51. DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild. ECCV 2020
  52. Self-similarity Student for Partial Label Histopathology Image Segmentation. ECCV 2020
  53. Robust Re-Identification by Multiple Views Knowledge Distillation. Porrello et al. ECCV 2020 [code]
  54. LabelEnc: A New Intermediate Supervision Method for Object Detection. Hao, Miao et al. arXiv:2007.03282
  55. Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer. Chen, Xinghao et al. ECCV 2020
  56. Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition. Si, Chenyang et al. ECCV 2020
  57. Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. Zhang, Yonggang et al. ICML 2020
  58. RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Mode. Zhang, Ziyue et al. arXiv:2007.07452
  59. Defocus Blur Detection via Depth Distillation. Cun, Xiaodong & Pun, Chi-Man. ECCV 2020 [code]
  60. Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer. Zhong, Yuanyi et al. ECCV 2020 [code]
  61. Weight Decay Scheduling and Knowledge Distillation for Active Learning. ECCV 2020
  62. Circumventing Outliers of AutoAugment with Knowledge Distillation. ECCV 2020
  63. Improving Face Recognition from Hard Samples via Distribution Distillation Loss. ECCV 2020
  64. Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition. ECCV 2020
  65. Self-similarity Student for Partial Label Histopathology Image Segmentation. Cheng, Hsien-Tzu et al. ECCV 2020
  66. Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation. Zhou, Yanning et al. arXiv:2007.10787 [code]
  67. Two-Level Residual Distillation based Triple Network for Incremental Object Detection. Yang, Dongbao et al. arXiv:2007.13428
  68. Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer. Liu, Yuting et al. ACM MM 2020
  69. Teacher-Critical Training Strategies for Image Captioning. Huang, Yiqing & Chen, Jiansheng. arXiv:2009.14405
  70. Object Relational Graph with Teacher-Recommended Learning for Video Captioning. Zhang, Ziqi et al. CVPR 2020
  71. Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection. Wang Yue et al. ECCV 2020
  72. Residual Feature Distillation Network for Lightweight Image Super-Resolution. Liu, Jie et al. ECCV 2020
  73. Intra-Utterance Similarity Preserving Knowledge Distillation for Audio Tagging. Interspeech 2020
  74. Federated Model Distillation with Noise-Free Differential Privacy. arXiv:2009.05537
  75. Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. Wang, Xudong et al. arXiv:2010.01809
  76. Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation. Yi, Tang & Yuan, Li. arXiv:2010.10027
  77. Multiresolution Knowledge Distillation for Anomaly Detection. Salehi et al. cvpr 2021
  78. Channel-wise Distillation for Semantic Segmentation. Shu, Changyong et al. arXiv: 2011.13256
  79. Teach me to segment with mixed supervision: Confident students become masters. Dolz, Jose et al. arXiv:2012.08051
  80. Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation. Xu, Chenxin et al. AAAI 2021 [code]
  81. Training data-efficient image transformers & distillation through attention. Touvron, Hugo et al. arXiv:2012.12877 [code]
  82. SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation. Peng, Can et al. arXiv:2012.15439
  83. PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning. Wang, Qin et al. AAAI 2021
  84. Diverse Knowledge Distillation for End-­to‐End Person Search. Zhang, Xinyu et al. AAAI 2021
  85. Enhanced Audio Tagging via Multi­‐ to Single­‐Modal Teacher­‐Student Mutual Learning. Yin, Yifang et al. AAAI 2021
  86. Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks. Li, Yige et al. ICLR 2021 [code]
  87. Unbiased Teacher for Semi-Supervised Object Detection. Liu, Yen-Cheng et al. ICLR 2021 [code]
  88. Localization Distillation for Object Detection. Zheng, Zhaohui et al. cvpr 2021 [code]
  89. Distilling Knowledge via Intermediate Classifier Heads. Aryan & Amirali. arXiv:2103.00497
  90. Distilling Object Detectors via Decoupled Features. (HUAWEI-Noah). CVPR 2021
  91. General Instance Distillation for Object Detection. Dai, Xing et al. CVPR 2021
  92. Multiresolution Knowledge Distillation for Anomaly Detection. Mohammadreza et al. CVPR 2021
  93. Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection. Wang, Guodong et al. arXiv:2103.04257
  94. Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition. Jaeyeon Jang & Chang Ouk Kim. IEEE 2021
  95. Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection. Hu, Hanzhe et al. CVPR 2021 [code]

for NLP & Data-Mining

  1. Patient Knowledge Distillation for BERT Model Compression. Sun, Siqi et al. arXiv:1908.09355
  2. TinyBERT: Distilling BERT for Natural Language Understanding. Jiao, Xiaoqi et al. arXiv:1909.10351
  3. Learning to Specialize with Knowledge Distillation for Visual Question Answering. NeurIPS 2018
  4. Knowledge Distillation for Bilingual Dictionary Induction. EMNLP 2017
  5. A Teacher-Student Framework for Maintainable Dialog Manager. EMNLP 2018
  6. Understanding Knowledge Distillation in Non-Autoregressive Machine Translation. arxiv 2019
  7. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Sanh, Victor et al. arXiv:1910.01108
  8. Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. Turc, Iulia et al. arXiv:1908.08962
  9. On Knowledge distillation from complex networks for response prediction. Arora, Siddhartha et al. NAACL 2019
  10. Distilling the Knowledge of BERT for Text Generation. arXiv:1911.03829v1
  11. Understanding Knowledge Distillation in Non-autoregressive Machine Translation. arXiv:1911.02727
  12. MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices. Sun, Zhiqing et al. ACL 2020
  13. Acquiring Knowledge from Pre-trained Model to Neural Machine Translation. Weng, Rongxiang et al. AAAI 2020
  14. TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval. Lu, Wenhao et al. KDD 2020
  15. Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation. Xu, Yige et al. arXiv:2002.10345
  16. FastBERT: a Self-distilling BERT with Adaptive Inference Time. Liu, Weijie et al. ACL 2020
  17. LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression. Mao, Yihuan et al. arXiv:2004.04124
  18. DynaBERT: Dynamic BERT with Adaptive Width and Depth. Hou, Lu et al. NeurIPS 2020
  19. Structure-Level Knowledge Distillation For Multilingual Sequence Labeling. Wang, Xinyu et al. ACL 2020
  20. Distilled embedding: non-linear embedding factorization using knowledge distillation. Lioutas, Vasileios et al. arXiv:1910.06720
  21. TinyMBERT: Multi-Stage Distillation Framework for Massive Multi-lingual NER. Mukherjee & Awadallah. ACL 2020
  22. Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation. Sun, Haipeng et al. arXiv:2004.10171
  23. Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Reimers, Nils & Gurevych, Iryna arXiv:2004.09813
  24. Distilling Knowledge for Fast Retrieval-based Chat-bots. Tahami et al. arXiv:2004.11045
  25. Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language. ACL 2020
  26. Local Clustering with Mean Teacher for Semi-supervised Learning. arXiv:2004.09665
  27. Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher. arXiv:2004.08780
  28. Syntactic Structure Distillation Pretraining For Bidirectional Encoders. arXiv: 2005.13482
  29. Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation. arXiv:2003.02877
  30. Distilling Neural Networks for Faster and Greener Dependency Parsing. arXiv:2006.00844
  31. Distilling Knowledge from Well-informed Soft Labels for Neural Relation Extraction. AAAI 2020 [code]
  32. More Grounded Image Captioning by Distilling Image-Text Matching Model. Zhou, Yuanen et al. CVPR 2020
  33. Multimodal Learning with Incomplete Modalities by Knowledge Distillation. Wang, Qi et al. KDD 2020
  34. Distilling the Knowledge of BERT for Sequence-to-Sequence ASR. Futami, Hayato et al. arXiv:2008.03822
  35. Contrastive Distillation on Intermediate Representations for Language Model Compression. Sun, Siqi et al. EMNLP 2020 [code]
  36. Noisy Self-Knowledge Distillation for Text Summarization. arXiv:2009.07032
  37. Simplified TinyBERT: Knowledge Distillation for Document Retrieval. arXiv:2009.07531
  38. Autoregressive Knowledge Distillation through Imitation Learning. arXiv:2009.07253
  39. BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance. EMNLP 2020 [code]
  40. Interpretable Embedding Procedure Knowledge Transfer. Seunghyun Lee et al. AAAI 2021 [code]
  41. LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding. Fu, Hao et al. AAAI 2021
  42. Towards Zero-Shot Knowledge Distillation for Natural Language Processing. Ahmad et al. arXiv:2012.15495
  43. Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains. Pan, Haojie et al. AAAI 2021
  44. Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation. Feng, Lingyun et al. AAAI 2021
  45. Label Confusion Learning to Enhance Text Classification Models. Guo, Biyang et al. AAAI 2021
  46. NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application. Wu, Chuhan et al. kdd 2021

for RecSys

  1. Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning. Liu, Xi et al. arXiv:2001.09595
  2. A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. Liu, Dugang et al. SIGIR 2020 [Sildes] [code]
  3. LightRec: a Memory and Search-Efficient Recommender System. Lian, Defu et al. WWW 2020
  4. Privileged Features Distillation at Taobao Recommendations. Xu, Chen et al. KDD 2020
  5. Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. WWW 2020
  6. Adversarial Distillation for Efficient Recommendation with External Knowledge. Chen, Xu et al. ACM Trans, 2018
  7. Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System. Tang, Jiaxi et al. SIGKDD 2018
  8. A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems. Pan, Yiteng et al. Neurocomputing 2019 [code]
  9. ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation. Mi, Fei et al. ACM RecSys 2020
  10. Ensembled CTR Prediction via Knowledge Distillation. Zhu, Jieming et al.(Huawei) CIKM 2020
  11. DE-RRD: A Knowledge Distillation Framework for Recommender System. Kang, Seongku et al. CIKM 2020 [code]
  12. Neural Compatibility Modeling with Attentive Knowledge Distillation. Song, Xuemeng et al. SIGIR 2018
  13. Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. Wang, Haoyu et al. IJCAI 2019
  14. Collaborative Distillation for Top-N Recommendation. Jae-woong Lee, et al. CIKM 2019
  15. Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation. Zhang Yuan et al. WSDM 2020
  16. UMEC:Unified Model and Embedding Compression for Efficient Recommendation Systems. ICLR 2021

Model Pruning or Quantization

  1. Accelerating Convolutional Neural Networks with Dominant Convolutional Kernel and Knowledge Pre-regression. ECCV 2016
  2. N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. Ashok, Anubhav et al. ICLR 2018
  3. Slimmable Neural Networks. Yu, Jiahui et al. ICLR 2018
  4. Co-Evolutionary Compression for Unpaired Image Translation. Shu, Han et al. ICCV 2019
  5. MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning. Liu, Zechun et al. ICCV 2019
  6. LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning. ICLR 2020
  7. Pruning with hints: an efficient framework for model acceleration. ICLR 2020
  8. Training convolutional neural networks with cheap convolutions and online distillation. arXiv:1909.13063
  9. Cooperative Pruning in Cross-Domain Deep Neural Network Compression. Chen, Shangyu et al. IJCAI 2019
  10. QKD: Quantization-aware Knowledge Distillation. Kim, Jangho et al. arXiv:1911.12491v1
  11. Neural Network Pruning with Residual-Connections and Limited-Data. Luo, Jian-Hao & Wu, Jianxin. CVPR 2020
  12. Training Quantized Neural Networks with a Full-precision Auxiliary Module. Zhuang, Bohan et al. CVPR 2020
  13. Towards Effective Low-bitwidth Convolutional Neural Networks. Zhuang, Bohan et al. CVPR 2018
  14. Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. Zhuang, Bohan et al. arXiv:1908.04680
  15. Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation. Le et al. arXiv:2006.11487 [code]
  16. Knowledge Distillation Beyond Model Compression. Choi, Arthur et al. arxiv:2007.01493
  17. Distillation Guided Residual Learning for Binary Convolutional Neural Networks. Ye, Jianming et al. ECCV 2020
  18. Cascaded channel pruning using hierarchical self-distillation. Miles & Mikolajczyk. BMVC 2020
  19. TernaryBERT: Distillation-aware Ultra-low Bit BERT. Zhang, Wei et al. EMNLP 2020
  20. Weight Distillation: Transferring the Knowledge in Neural Network Parameters. arXiv:2009.09152
  21. Stochastic Precision Ensemble: Self-­‐Knowledge Distillation for Quantized Deep Neural Networks. Boo, Yoonho et al. AAAI 2021
  22. Binary Graph Neural Networks. Bahri, Mehdi et al. CVPR 2021

Beyond

  1. Do deep nets really need to be deep?. Ba,Jimmy, and Rich Caruana. NeurIPS 2014
  2. When Does Label Smoothing Help? Müller, Rafael, Kornblith, and Hinton. NeurIPS 2019
  3. Towards Understanding Knowledge Distillation. Phuong, Mary and Lampert, Christoph. ICML 2019
  4. Harnessing deep neural networks with logical rules. ACL 2016
  5. Adaptive Regularization of Labels. Ding, Qianggang et al. arXiv:1908.05474
  6. Knowledge Isomorphism between Neural Networks. Liang, Ruofan et al. arXiv:1908.01581
  7. (survey)Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation. arXiv:1912.13179
  8. Understanding and Improving Knowledge Distillation. Tang, Jiaxi et al. arXiv:2002.03532
  9. The State of Knowledge Distillation for Classification. Ruffy, Fabian and Chahal, Karanbir. arXiv:1912.10850 [code]
  10. Explaining Knowledge Distillation by Quantifying the Knowledge. Zhang, Quanshi et al. CVPR 2020
  11. DeepVID: deep visual interpretation and diagnosis for image classifiers via knowledge distillation. IEEE Trans, 2019.
  12. On the Unreasonable Effectiveness of Knowledge Distillation: Analysis in the Kernel Regime. Rahbar, Arman et al. arXiv:2003.13438
  13. (survey)Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks. Wang, Lin & Yoon, Kuk-Jin. arXiv:2004.05937
  14. Why distillation helps: a statistical perspective. arXiv:2005.10419
  15. Transferring Inductive Biases through Knowledge Distillation. Abnar, Samira et al. arXiv:2006.00555
  16. Does label smoothing mitigate label noise? Lukasik, Michal et al. ICML 2020
  17. An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation. Das, Deepan et al. arXiv:2006.03810
  18. Knowledge Distillation: A Survey. Gou, Jianping et al. IJCV 2021
  19. Does Adversarial Transferability Indicate Knowledge Transferability? Liang, Kaizhao et al. arXiv:2006.14512
  20. On the Demystification of Knowledge Distillation: A Residual Network Perspective. Jha et al. arXiv:2006.16589
  21. Enhancing Simple Models by Exploiting What They Already Know. Dhurandhar et al. ICML 2020
  22. Feature-Extracting Functions for Neural Logic Rule Learning. Gupta & Robles-Kelly.arXiv:2008.06326
  23. On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective. SeongUk et al. arXiv:2009.04120
  24. Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. Ji, Guangda & Zhu, Zhanxing. NeurIPS 2020
  25. In Defense of Feature Mimicking for Knowledge Distillation. Wang, Guo-Hua et al. arXiv:2011.0142
  26. Solvable Model for Inheriting the Regularization through Knowledge Distillation. Luca Saglietti & Lenka Zdeborova. arXiv:2012.00194
  27. Undistillable: Making A Nasty Teacher That CANNOT Teach Students. ICLR 2021
  28. Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning. Allen-Zhu, Zeyuan & Li, Yuanzhi.(Microsoft) arXiv:2012.09816
  29. Student-Teacher Learning from Clean Inputs to Noisy Inputs. Hong, Guanzhe et al. CVPR 2021

Distiller Tools

  1. Neural Network Distiller: A Python Package For DNN Compression Research. arXiv:1910.12232
  2. TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing. HIT and iFLYTEK. arXiv:2002.12620
  3. torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation.
  4. KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization. Shen, Het et al. arXiv:2011.14691
  5. Knowledge-Distillation-Zoo
  6. RepDistiller
  7. classification distiller

Note: All papers' pdf can be found and downloaded on arXiv, Bing or Google.

Source: https://github.com/FLHonker/Awesome-Knowledge-Distillation

Thanks for all contributors:

yuang lioutasb KaiyuYue avatar cardwing jaywonchung ZainZhao avatar

Contact: Yuang Liu(frankliu624outlook.com), ECNU. Supervisor: Wei Zhang, Jun Wang.

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