awesome-automl-and-lightweight-models
github.com/guan-yuan/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.
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1.) Neural Architecture Search2.) Lightweight Structures3.) Model Compression & Acceleration4.) Hyperparameter OptimizationModel AnalyzerReferences
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3.) Model Compression & Acceleration
- aaron-xichen/pytorch-playground[Projects]
[Pytorch]
- AMC: AutoML for Model Compression and Acceleration on Mobile Devices[Papers]
[ECCV 2018]
- andravin/wincnn[Papers]
[Python]
- Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy[Papers]
[ICLR 2018]
- AutoML for Model Compression (AMC): Trials and Tribulations[Papers]
[Pytorch]
- Channel Pruning for Accelerating Very Deep Neural Networks[Papers]
[ICCV 2017]
References
1.) Neural Architecture Search
- AnjieZheng/MnasNet-PyTorch[Papers]
[Pytorch]
- ASAP: Architecture Search, Anneal and Prune[Papers]
[2019/04]
- Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation[Papers]
[2019/01]
- Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes[Papers]
[IEEE Access 2019]
- carpedm20/ENAS-pytorch[Papers]
[Pytorch]
- chenxi116/PNASNet.pytorch[Papers]
[Pytorch]
4.) Hyperparameter Optimization
- Ax (Adaptive Experimentation Platform)[Projects]
[PyTorch]
- Bayesian optimization[Tutorials/Blogs]
- BoTorch[Projects]
[PyTorch]
- dragonfly/dragonfly[Projects]
[Python]
- Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features[Papers]
[NeurIPS 2018]
- Google vizier: A service for black-box optimization[Papers]
[SIGKDD 2017]
2.) Lightweight Structures
- BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation[Papers]
[ECCV 2018]
- CGNet: A Light-weight Context Guided Network for Semantic Segmentation[Papers]
[2019/04]
- dlyldxwl/fssd.pytorch[Papers]
[Pytorch]
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks[Papers]
[ICML 2019]
- ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation[Papers]
[T-ITS 2017]
- Eromera/erfnet_pytorch[Papers]
[Pytorch]
Model Analyzer
- Lyken17/pytorch-OpCounter
[Pytorch]
- Netscope CNN Analyzer
[Caffe]
- sksq96/pytorch-summary
[Pytorch]
- sovrasov/flops-counter.pytorch
[Pytorch]
Showing a sample of 144 resources. View the full list on GitHub →