Github项目推荐 | AutoML与轻量模型列表

awesome-AutoML-and-Lightweight-Models

by guan-yuan

本项目旨在为自动化研究(特别是轻量级模型)提供信息。有兴趣的同学可以进行收藏或者在Github中推荐/提交项目(论文、项目仓库等)。

高质量(最新)AutoML项目和轻量级模型汇总列表,列表包括以下内容:

  • 1.神经结构搜索
  • 2.轻量级结构
  • 3.模型压缩和加速
  • 4.超参数优化
  • 5.自动化特征工程

Github项目地址:

https://github.com/guan-yuan/awesome-AutoML-and-Lightweight-Models

详细论文内容,请点击阅读原文后点击相关链接访问。

1 神经结构搜索

[论文]

梯度:

  • ASAP: Architecture Search, Anneal and Prune | [2019/04]
  • Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours | [2019/04]
  • dstamoulis/single-path-nas | [Tensorflow]
  • Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes | [IEEE Access 2019]
  • sharpDARTS: Faster and More Accurate Differentiable Architecture Search | [2019/03]
  • Learning Implicitly Recurrent CNNs Through Parameter Sharing | [ICLR 2019]
  • lolemacs/soft-sharing | [Pytorch]
  • Probabilistic Neural Architecture Search | [2019/02]
  • Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation | [2019/01]
  • SNAS: Stochastic Neural Architecture Search | [ICLR 2019]
  • FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | [2018/12]
  • Neural Architecture Optimization | [NIPS 2018]
  • renqianluo/NAO | [Tensorflow]
  • DARTS: Differentiable Architecture Search | [2018/06]
  • quark0/darts | [Pytorch]
  • khanrc/pt.darts | [Pytorch]
  • dragen1860/DARTS-PyTorch | [Pytorch]

强化学习:

  • Template-Based Automatic Search of Compact Semantic Segmentation Architectures | [2019/04]
  • Understanding Neural Architecture Search Techniques | [2019/03]
  • Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search | [2019/01]
  • falsr/FALSR | [Tensorflow]
  • Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search | [2019/01]
  • moremnas/MoreMNAS | [Tensorflow]
  • ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware | [ICLR 2019]
  • MIT-HAN-LAB/ProxylessNAS | [Pytorch, Tensorflow]
  • Transfer Learning with Neural AutoML | [NIPS 2018]
  • Learning Transferable Architectures for Scalable Image Recognition | [2018/07]
  • wandering007/nasnet-pytorch | [Pytorch]
  • tensorflow/models/research/slim/nets/nasnet | [Tensorflow]
  • MnasNet: Platform-Aware Neural Architecture Search for Mobile | [2018/07]
  • AnjieZheng/MnasNet-PyTorch | [Pytorch]
  • Practical Block-wise Neural Network Architecture Generation | [CVPR 2018]
  • Efficient Neural Architecture Search via Parameter Sharing | [ICML 2018]
  • melodyguan/enas | [Tensorflow]
  • carpedm20/ENAS-pytorch | [Pytorch]
  • Efficient Architecture Search by Network Transformation | [AAAI 2018]

进化算法:

  • Single Path One-Shot Neural Architecture Search with Uniform Sampling | [2019/04]
  • DetNAS: Neural Architecture Search on Object Detection | [2019/03]
  • The Evolved Transformer | [2019/01]
  • Designing neural networks through neuroevolution | [Nature Machine Intelligence 2019]
  • EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search | [2019/01]
  • Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution | [ICLR 2019]

SMBO(Sequential Model-Based Optimization - 基于序列模型的优化):

  • MFAS: Multimodal Fusion Architecture Search | [CVPR 2019]
  • DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures | [ECCV 2018]
  • Progressive Neural Architecture Search | [ECCV 2018]
  • titu1994/progressive-neural-architecture-search | [Keras, Tensorflow]
  • chenxi116/PNASNet.pytorch | [Pytorch]

随机搜索:

  • Exploring Randomly Wired Neural Networks for Image Recognition | [2019/04]
  • Searching for Efficient Multi-Scale Architectures for Dense Image Prediction | [NIPS 2018]

超网络:

  • Graph HyperNetworks for Neural Architecture Search | [ICLR 2019]

贝叶斯优化:

  • Inductive Transfer for Neural Architecture Optimization | [2019/03]

偏序修剪:

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

知识提炼:

  • Improving Neural Architecture Search Image Classifiers via Ensemble Learning | [2019/03]

[项目]

  • Microsoft/nni | [Python]

2 轻量级结构

[论文]

分割:

  • ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network | [2018/11]
  • sacmehta/ESPNetv2 | [Pytorch]
  • ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation | [ECCV 2018]
  • sacmehta/ESPNet | [Pytorch]
  • BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation | [ECCV 2018]
  • ooooverflow/BiSeNet | [Pytorch]
  • ycszen/TorchSeg | [Pytorch]
  • ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation | [T-ITS 2017]
  • Eromera/erfnet_pytorch | [Pytorch]

物体检测:

  • Pooling Pyramid Network for Object Detection | [2018/09]
  • tensorflow/models | [Tensorflow]
  • Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages | [BMVC 2018]
  • lyxok1/Tiny-DSOD | [Caffe]
  • Pelee: A Real-Time Object Detection System on Mobile Devices | [NeurIPS 2018]
  • Robert-JunWang/Pelee | [Caffe]
  • Robert-JunWang/PeleeNet | [Pytorch]
  • Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV 2018]
  • ruinmessi/RFBNet | [Pytorch]
  • ShuangXieIrene/ssds.pytorch | [Pytorch]
  • lzx1413/PytorchSSD | [Pytorch]
  • FSSD: Feature Fusion Single Shot Multibox Detector | [2017/12]
  • ShuangXieIrene/ssds.pytorch | [Pytorch]
  • lzx1413/PytorchSSD | [Pytorch]
  • dlyldxwl/fssd.pytorch | [Pytorch]
  • Feature Pyramid Networks for Object Detection | [CVPR 2017]
  • tensorflow/models | [Tensorflow]

3 模型压缩和加速

[论文]

压缩:

  • Slimmable Neural Networks | [ICLR 2019]
  • JiahuiYu/slimmable_networks | [Pytorch]
  • AMC: AutoML for Model Compression and Acceleration on Mobile Devices | [ECCV 2018]
  • AutoML for Model Compression (AMC): Trials and Tribulations | [Pytorch]
  • Learning Efficient Convolutional Networks through Network Slimming | [ICCV 2017]
  • foolwood/pytorch-slimming | [Pytorch]
  • Channel Pruning for Accelerating Very Deep Neural Networks | [ICCV 2017]
  • yihui-he/channel-pruning | [Caffe]
  • Pruning Convolutional Neural Networks for Resource Efficient Inference | [ICLR 2017]
  • jacobgil/pytorch-pruning | [Pytorch]
  • Pruning Filters for Efficient ConvNets | [ICLR 2017]

加速:

  • Fast Algorithms for Convolutional Neural Networks | [CVPR 2016]
  • andravin/wincnn | [Python]

[项目]

  • NervanaSystems/distiller | [Pytorch]
  • Tencent/PocketFlow | [Tensorflow]

[教程/博客]

  • Introducing the CVPR 2018 On-Device Visual Intelligence Challenge

4 超参数优化

[论文]

  • Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly | [2019/03]
  • dragonfly/dragonfly
  • Google vizier: A service for black-box optimization | [SIGKDD 2017]

[项目]

  • Microsoft/nni | [Python]
  • dragonfly/dragonfly | [Python]

[教程/博客]

  • Hyperparameter tuning in Cloud Machine Learning Engine using Bayesian Optimization
  • Overview of Bayesian Optimization
  • Bayesian optimization
  • krasserm/bayesian-machine-learning | [Python]

5 自动化特征工程

[模型分析器]

  • Netscope CNN Analyzer | [Caffe]
  • sksq96/pytorch-summary | [Pytorch]
  • Lyken17/pytorch-OpCounter | [Pytorch]

[参考]

  • LITERATURE ON NEURAL ARCHITECTURE SEARCH
  • handong1587/handong1587.github.io
  • hibayesian/awesome-automl-papers
  • mrgloom/awesome-semantic-segmentation
  • amusi/awesome-object-detection

原文发布于微信公众号 - AI研习社(okweiwu)

原文发表时间:2019-05-04

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