（转）AutoML 与轻量模型大列表： awesome-AutoML-and-Lightweight-Models
Awesome-AutoML-and-Lightweight-Models
原文：http://bbs.cvmart.net/articles/414/zi-yuan-automl-yu-qing-liang-mo-xing-da-lie-biao
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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 & 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:
-
Searching for A Robust Neural Architecture in Four GPU Hours | [CVPR 2019]
- D-X-Y/GDAS | [Pytorch]
-
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]
Reinforcement Learning:
-
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]
Evolutionary Algorithm:
-
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:
-
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]
Random Search:
-
Exploring Randomly Wired Neural Networks for Image Recognition | [2019/04]
-
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction | [NIPS 2018]
Hypernetwork:
- Graph HyperNetworks for Neural Architecture Search | [ICLR 2019]
Bayesian Optimization:
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
[Projects]
- Microsoft/nni | [Python]
2.) Lightweight Structures
[Papers]
Backbone:
- Searching for MobileNetV3 | [2019/05]
- kuan-wang/pytorch-mobilenet-v3 | [Pytorch]
- leaderj1001/MobileNetV3-Pytorch | [Pytorch]
Segmentation:
-
CGNet: A Light-weight Context Guided Network for Semantic Segmentation | [2019/04]
- wutianyiRosun/CGNet | [Pytorch]
-
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]
Object Detection:
-
ThunderNet: Towards Real-time Generic Object Detection | [2019/03]
-
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.) Model Compression & Acceleration
[Papers]
Compression:
-
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks | [ICLR 2019]
- google-research/lottery-ticket-hypothesis | [Tensorflow]
-
Rethinking the Value of Network Pruning | [ICLR 2019]
-
Slimmable Neural Networks | [ICLR 2019]
- JiahuiYu/slimmable_networks | [Pytorch]
-
AMC: AutoML for Model Compression and Acceleration on Mobile Devices | [ECCV 2018]
-
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]
Acceleration:
- Fast Algorithms for Convolutional Neural Networks | [CVPR 2016]
- andravin/wincnn | [Python]
[Projects]
- NervanaSystems/distiller | [Pytorch]
- Tencent/PocketFlow | [Tensorflow]
[Tutorials/Blogs]
4.) Hyperparameter Optimization
[Papers]
-
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly | [2019/03]
-
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features | [NeurIPS 2018]
-
Google vizier: A service for black-box optimization | [SIGKDD 2017]
[Projects]
- BoTorch | [PyTorch]
- Ax (Adaptive Experimentation Platform) | [PyTorch]
- Microsoft/nni | [Python]
- dragonfly/dragonfly | [Python]
[Tutorials/Blogs]
-
Hyperparameter tuning in Cloud Machine Learning Engine using Bayesian Optimization
-
- krasserm/bayesian-machine-learning | [Python]
5.) Automated Feature Engineering
Model Analyzer
-
Netscope CNN Analyzer | [Caffe]
-
sksq96/pytorch-summary | [Pytorch]
-
Lyken17/pytorch-OpCounter | [Pytorch]
-
sovrasov/flops-counter.pytorch | [Pytorch]
References
- LITERATURE ON NEURAL ARCHITECTURE SEARCH
- handong1587/handong1587.github.io
- hibayesian/awesome-automl-papers
- mrgloom/awesome-semantic-segmentation
- amusi/awesome-object-detection
