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:notebook: Notes and summaries of various ML, Computer Vision & NLP papers.

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ML Papers

This repo contains notes and short summaries of some ML related papers I come across, organized by subjects and the summaries are in the form of PDFs.

Self-Supervised & Contrastive Learning

  • Self-Supervised Relational Reasoning for Representation Learning (2020): [Paper] [Notes]
  • Big Self-Supervised Models are Strong Semi-Supervised Learners (2020) [Paper] [Notes]
  • Debiased Contrastive Learning (2020) [Paper] [Notes]
  • Selfie: Self-supervised Pretraining for Image Embedding (2019): [Paper] [Notes]
  • Self-Supervised Representation Learning by Rotation Feature Decoupling (2019): [Paper] [Notes]
  • Revisiting Self-Supervised Visual Representation Learning (2019): [Paper] [Notes]
  • AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations (2019): [Paper] [Notes]
  • Boosting Self-Supervised Learning via Knowledge Transfer (2018): [Paper] [Notes]
  • Self-Supervised Feature Learning by Learning to Spot Artifacts (2018): [Paper] [Notes]
  • Unsupervised Representation Learning by Predicting Image Rotations (2018): [Paper] [Notes]
  • Cross Pixel Optical-Flow Similarity for Self-Supervised Learning (2018): [Paper] [Notes]
  • Multi-task Self-Supervised Visual Learning (2017): [Paper] [Notes]
  • Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction (2017): [Paper] [Notes]
  • Colorization as a Proxy Task for Visual Understanding (2017): [Paper] [Notes]
  • Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles (2017): [Paper] [Notes]
  • Unsupervised Visual Representation Learning by Context Prediction (2016): [Paper] [Notes]
  • Colorful image colorization (2016): [Paper] [Notes]
  • Learning visual groups from co-occurrences in space and time (2015): [Paper] [Notes]
  • Discriminative unsupervised feature learning with exemplar convolutional neural networks (2015): [Paper] [Notes]

Semi-Supervised Learning

  • Negative sampling in semi-supervised learning (2020): [Paper] [Notes]
  • Time-Consistent Self-Supervision for Semi-Supervised Learning (2020): [Paper] [Notes]
  • Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning (2019): [Paper] [Notes]
  • S4L: Self-Supervised Semi-Supervised Learning (2019): [Paper] [Notes]
  • Semi-Supervised Learning by Augmented Distribution Alignment (2019): [Paper] [Notes]
  • MixMatch: A Holistic Approach toSemi-Supervised Learning (2019): [Paper] [Notes]
  • Unsupervised Data Augmentation (2019): [Paper] [Notes]
  • Interpolation Consistency Training for Semi-Supervised Learning (2019): [Paper] [Notes]
  • Deep Co-Training for Semi-Supervised Image Recognition (2018): [Paper] [Notes]
  • Unifying semi-supervised and robust learning by mixup (2019): [Paper] [Notes]
  • Realistic Evaluation of Deep Semi-Supervised Learning Algorithms (2018): [Paper] [Notes]
  • Semi-Supervised Sequence Modeling with Cross-View Training (2018): [Paper] [Notes]
  • Virtual Adversarial Training (2017): [Paper] [Notes]
  • Mean teachers are better role models (2017): [Paper] [Notes]
  • Temporal Ensembling for Semi-Supervised Learning (2017): [Paper] [Notes]
  • Semi-Supervised Learning with Ladder Networks (2015): [Paper] [Notes]

Domain Adaptation, Domain & Out-of-Distribution Generalization

  • Rethinking Distributional Matching Based Domain Adaptation (2020): [Paper] [Notes]
  • Transferability vs. Discriminability: Batch Spectral Penalization (2019): [Paper] [Notes]
  • On Learning Invariant Representations for Domain Adaptation (2019): [Paper] [Notes]
  • Universal Domain Adaptation (2019): [Paper] [Notes]
  • Transferable Adversarial Training (2019): [Paper] [Notes]
  • Multi-Adversarial Domain Adaptation (2018): [Paper] [Notes]
  • Conditional Adversarial Domain Adaptation (2018): [Paper] [Notes]
  • Learning Adversarially Fair and Transferable Representations (2018): [Paper] [Notes]
  • What is the Effect of Importance Weighting in Deep Learning? (2018): [Paper] [Notes]


  • Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models (2021): [Paper] [Notes]
  • Transformer Interpretability Beyond Attention Visualization (2020): [Paper] [Notes]
  • What shapes feature representations Exploring datasets architectures and training (2020): [Paper] [Notes]
  • Attention-based Dropout Layer for Weakly Supervised Object Localization (2019): [Paper] [Notes]
  • Attention is not Explanation (2019): [Paper] [Notes]
  • SmoothGrad removing noise by adding noise (2017): [Paper] [Notes]
  • Axiomatic Attribution for Deep Networks (2017): [Paper] [Notes]
  • Attention Branch Network: Learning of Attention Mechanism for Visual Explanation (2019): [Paper] [Notes]
  • Paying More Attention to Attention: Improving the Performance of CNNs via Attention Transfer (2016): [Paper] [Notes]

Natural Language Processing (NLP)

  • Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data (2020): [Paper] [Notes]
  • Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning (2021): [Paper] [Notes]
  • BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (2020): [Paper] [Notes]
  • FreeLB: Enhanced Adversarial Training for Natural Language Understanding (2020): [Paper] [Notes]
  • MixText: Linguistically-Informed Interpolation for Semi-Supervised Text Classification (2020): [Paper] [Notes]

Generative Modeling

  • Generative Pretraining from Pixels (2020): [Paper] [Notes]
  • Consistency Regularization for Generative Adversarial Networks (2020): [Paper] [Notes]

Unsupervised Learning

  • Invariant Information Clustering for Unsupervised Image Classification and Segmentation (2019): [Paper] [Notes]
  • Deep Clustering for Unsupervised Learning of Visual Feature (2018): [Paper] [Notes]

Semantic Segmentation

  • DeepLabv3+: Encoder-Decoder with Atrous Separable Convolution (2018): [Paper] [Notes]
  • Large Kernel Matter, Improve Semantic Segmentation by Global Convolutional Network (2017): [Paper] [Notes]
  • Understanding Convolution for Semantic Segmentation (2018): [Paper] [Notes]
  • Rethinking Atrous Convolution for Semantic Image Segmentation (2017): [Paper] [Notes]
  • RefineNet: Multi-path refinement networks for high-resolution semantic segmentation (2017): [Paper] [Notes]
  • Pyramid Scene Parsing Network (2017): [Paper] [Notes]
  • SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for ImageSegmentation (2016): [Paper] [Notes]
  • ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (2016): [Paper] [Notes]
  • Attention to Scale: Scale-aware Semantic Image Segmentation (2016): [Paper] [Notes]
  • Deeplab: semantic image segmentation with DCNN, atrous convs and CRFs (2016): [Paper] [Notes]
  • U-Net: Convolutional Networks for Biomedical Image Segmentation (2015): [Paper] [Notes]
  • Fully Convolutional Networks for Semantic Segmentation (2015): [Paper] [Notes]
  • Hypercolumns for object segmentation and fine-grained localization (2015): [Paper] [Notes]

Weakly- and Semi-supervised Semantic segmentation

  • Box-driven Class-wise Region Masking and Filling Rate Guided Loss (2019): [Paper] [Notes]
  • FickleNet: Weakly and Semi-supervised Semantic Segmentation using Stochastic Inference (2019): [Paper] [Notes]
  • Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (2018): [Paper] [Notes]
  • Learning Pixel-level Semantic Affinity with Image-level Supervision (2018): [Paper] [Notes]
  • Object Region Mining with Adversarial Erasing (2018): [Paper] [Notes]
  • Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Segmentation (2018): [Paper] [Notes]
  • Tell Me Where to Look: Guided Attention Inference Network (2018): [Paper] [Notes]
  • Semi Supervised Semantic Segmentation Using Generative Adversarial Network (2017): [Paper] [Notes]
  • Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation (2015): [Paper] [Notes]
  • Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation (2015): [Paper] [Notes]

Information Retrieval

  • VSE++: Improving Visual-Semantic Embeddings with Hard Negatives (2018): [Paper] [Notes]

Graph Neural Network

  • Pixels to Graphs by Associative Embedding (2017): [Paper] [Notes]
  • Associative Embedding: End-to-End Learning forJoint Detection and Grouping (2017): [Paper] [Notes]
  • Interaction Networks for Learning about Objects , Relations and Physics (2016): [Paper] [Notes]
  • DeepWalk: Online Learning of Social Representation (2014): [Paper] [Notes]
  • The graph neural network model (2009): [Paper] [Notes]


  • Manifold Mixup: Better Representations by Interpolating Hidden States (2018): [Paper] [Notes]

Deep learning Methods & Models

Document analysis and segmentation

  • dhSegment: A generic deep-learning approach for document segmentation (2018): [Paper] [Notes]
  • Learning to extract semantic structure from documents using multimodal fully convolutional neural networks (2017): [Paper] [Notes]
  • Page Segmentation for Historical Handwritten Document Images Using Conditional Random Fields (2016): [Paper] [Notes]
  • ICDAR 2015 competition on text line detection in historical documents (2015): [Paper] [Notes]
  • Handwritten text line segmentation using Fully Convolutional Network (2017): [Paper] [Notes]
  • Deep Neural Networks for Large Vocabulary Handwritten Text Recognition (2015): [Paper] [Notes]
  • Page Segmentation of Historical Document Images with Convolutional Autoencoders (2015): [Paper] [Notes]

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