A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Model Compression & Acceleration and Lightweight Structures, 3.) Hyperparameter Optimization, 4.) 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.
Gradient:
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Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation | [2019/01]
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SNAS: Stochastic Neural Architecture Search | [ICLR 2019]
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FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | [2018/12]
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Neural Architecture Optimization | [NIPS 2018]
- renqianluo/NAO | [Tensorflow]
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DARTS: Differentiable Architecture Search | [2018/06]
- quark0/darts | [Pytorch]
- khanrc/pt.darts | [Pytorch]
- dragen1860/DARTS-PyTorch | [Pytorch]
Reinforcement Learning:
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Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search | [2019/01]
- falsr/FALSR | [Tensorflow]
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Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search | [2019/01]
- moremnas/MoreMNAS | [Tensorflow]
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ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware | [ICLR 2019]
- MIT-HAN-LAB/ProxylessNAS | [Pytorch, Tensorflow]
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Transfer Learning with Neural AutoML | [NIPS 2018]
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Learning Transferable Architectures for Scalable Image Recognition | [2018/07]
- wandering007/nasnet-pytorch | [Pytorch]
- tensorflow/models/research/slim/nets/nasnet | [Tensorflow]
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MnasNet: Platform-Aware Neural Architecture Search for Mobile | [2018/07]
- AnjieZheng/MnasNet-PyTorch | [Pytorch]
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Practical Block-wise Neural Network Architecture Generation | [CVPR 2018]
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Efficient Neural Architecture Search via Parameter Sharing | [ICML 2018]
- melodyguan/enas | [Tensorflow]
- carpedm20/ENAS-pytorch | [Pytorch]
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Efficient Architecture Search by Network Transformation | [AAAI 2018]
Evolutionary Algorithm:
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The Evolved Transformer | [2019/01]
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Designing neural networks through neuroevolution | [Nature Machine Intelligence 2019]
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EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search | [2019/01]
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Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution | [ICLR 2019]
SMBO:
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DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures | [ECCV 2018]
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Progressive Neural Architecture Search | [ECCV 2018]
- titu1994/progressive-neural-architecture-search | [Keras, Tensorflow]
- chenxi116/PNASNet.pytorch | [Pytorch]
Random Search:
Hypernetwork:
- Graph HyperNetworks for Neural Architecture Search | [ICLR 2019]
- Microsoft/nni | [Python]
Segmentation:
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ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network | [2018/11]
- sacmehta/ESPNetv2 | [Pytorch]
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ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation | [ECCV 2018]
- sacmehta/ESPNet | [Pytorch]
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ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation | [T-ITS 2017]
- Eromera/erfnet_pytorch | [Pytorch]
Object Detection:
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Pooling Pyramid Network for Object Detection | [2018/09]
- tensorflow/models | [Tensorflow]
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Pelee: A Real-Time Object Detection System on Mobile Devices | [ICLR 2018 workshop]
- Robert-JunWang/Pelee | [Caffe]
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Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV 2018]
- ruinmessi/RFBNet | [Pytorch]
- ShuangXieIrene/ssds.pytorch | [Pytorch]
- lzx1413/PytorchSSD | [Pytorch]
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FSSD: Feature Fusion Single Shot Multibox Detector | [2017/12]
- ShuangXieIrene/ssds.pytorch | [Pytorch]
- lzx1413/PytorchSSD | [Pytorch]
- dlyldxwl/fssd.pytorch | [Pytorch]
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Feature Pyramid Networks for Object Detection | [CVPR 2017]
- tensorflow/models | [Tensorflow]
Compression:
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AMC: AutoML for Model Compression and Acceleration on Mobile Devices | [ECCV 2018]
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Learning Efficient Convolutional Networks through Network Slimming | [ICCV 2017]
- foolwood/pytorch-slimming | [Pytorch]
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Channel Pruning for Accelerating Very Deep Neural Networks | [ICCV 2017]
- yihui-he/channel-pruning | [Caffe]
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Pruning Convolutional Neural Networks for Resource Efficient Inference | [ICLR 2017]
- jacobgil/pytorch-pruning | [Pytorch]
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Pruning Filters for Efficient ConvNets | [ICLR 2017]
Acceleration:
- Fast Algorithms for Convolutional Neural Networks | [CVPR 2016]
- andravin/wincnn | [Python]
- NervanaSystems/distiller | [Pytorch]
- Tencent/PocketFlow | [Tensorflow]
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Introducing the CVPR 2018 On-Device Visual Intelligence Challenge
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pytorch_flops_benchmark | [Pytorch]
- Google vizier: A service for black-box optimization | [SIGKDD 2017]
- Microsoft/nni | [Python]