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guan-yuan
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A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

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awesome-AutoML-and-Lightweight-Models

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

This repo is aimed to provide the info for AutoML research (especially for the lightweight models). Welcome to PR the works (papers, repositories) that are missed by the repo.

1.) Neural Architecture Search

[Papers]

Gradient: - When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks | [CVPR 2020] + gmh14/RobNets | [Pytorch]

Reinforcement Learning:
- Template-Based Automatic Search of Compact Semantic Segmentation Architectures | [2019/04]

Evolutionary Algorithm: - Single Path One-Shot Neural Architecture Search with Uniform Sampling | [2019/04]

SMBO: - MFAS: Multimodal Fusion Architecture Search | [CVPR 2019]

Random Search: - Exploring Randomly Wired Neural Networks for Image Recognition | [2019/04]

Hypernetwork: - Graph HyperNetworks for Neural Architecture Search | [ICLR 2019]

Bayesian Optimization: - Inductive Transfer for Neural Architecture Optimization | [2019/03]

Partial Order Pruning - Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search | [CVPR 2019] + lixincn2015/Partial-Order-Pruning | [Caffe]

Knowledge Distillation - Improving Neural Architecture Search Image Classifiers via Ensemble Learning | [2019/03]

[Projects]

2.) Lightweight Structures

[Papers]

Image Classification: - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | [ICML 2019] + tensorflow/tpu/models/official/efficientnet/ | [Tensorflow] + lukemelas/EfficientNet-PyTorch | [Pytorch]

Semantic Segmentation: - CGNet: A Light-weight Context Guided Network for Semantic Segmentation | [2019/04] + wutianyiRosun/CGNet | [Pytorch]

Object Detection: - ThunderNet: Towards Real-time Generic Object Detection | [2019/03]

3.) Model Compression & Acceleration

[Papers]

Pruning: - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks | [ICLR 2019] + google-research/lottery-ticket-hypothesis | [Tensorflow]

Quantization: - Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets | [ICLR 2019]

Knowledge Distillation - Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy | [ICLR 2018]

Acceleration: - Fast Algorithms for Convolutional Neural Networks | [CVPR 2016] + andravin/wincnn | [Python]

[Projects]

[Tutorials/Blogs]

4.) Hyperparameter Optimization

[Papers]

[Projects]

[Tutorials/Blogs]

5.) Automated Feature Engineering

Model Analyzer

References

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