https://github.com/huawei-noah/efficient-computing
Efficient computing methods developed by Huawei Noah's Ark Lab
https://github.com/huawei-noah/efficient-computing
binary-neural-networks knowledge-distillation model-compression pruning quantization self-supervised
Last synced: 28 days ago
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Efficient computing methods developed by Huawei Noah's Ark Lab
- Host: GitHub
- URL: https://github.com/huawei-noah/efficient-computing
- Owner: huawei-noah
- Created: 2019-09-04T10:39:36.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-11-05T13:53:24.000Z (7 months ago)
- Last Synced: 2025-04-04T23:02:13.462Z (2 months ago)
- Topics: binary-neural-networks, knowledge-distillation, model-compression, pruning, quantization, self-supervised
- Language: Jupyter Notebook
- Homepage:
- Size: 100 MB
- Stars: 1,257
- Watchers: 23
- Forks: 218
- Open Issues: 20
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Metadata Files:
- Readme: README.md
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README
# Efficient Computing
This repo is a collection of Efficient-Computing methods developed by Huawei Noah's Ark Lab.
- [Data-Efficient-Model-Compression](https://github.com/huawei-noah/Efficient-Computing/tree/master/Data-Efficient-Model-Compression) is a series of compression methods with no or little training data.
- [BinaryNetworks](https://github.com/huawei-noah/Efficient-Computing/tree/master/BinaryNetworks): Binary neural networks including [AdaBin (ECCV22)](https://arxiv.org/abs/2208.08084).
- [Distillation](https://github.com/huawei-noah/Efficient-Computing/tree/master/Distillation): Knowledge distillation methods including [ManifoldKD (NeurIPS22)](https://arxiv.org/pdf/2107.01378.pdf) and [VanillaKD (NeurIPS23)](https://arxiv.org/abs/2305.15781).
- [Pruning](https://github.com/huawei-noah/Efficient-Computing/tree/master/Pruning): Network pruning methods including [GAN-pruning (ICCV19)](https://arxiv.org/abs/1907.10804), [SCOP (NeurIPS20)](https://arxiv.org/abs/2010.10732), [ManiDP (CVPR21)](https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper.pdf), and [RPG (NeurIPS23)](https://proceedings.neurips.cc/paper_files/paper/2023/hash/040ace837dd270a87055bb10dd7c0392-Abstract-Conference.html).
- [Quantization](https://github.com/huawei-noah/Efficient-Computing/tree/master/Quantization): Model quantization methods including [DynamicQuant (CVPR22)](https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Instance-Aware_Dynamic_Neural_Network_Quantization_CVPR_2022_paper.html).
- [Self-supervised](https://github.com/huawei-noah/Efficient-Computing/tree/master/Self-supervised): self-supervised learning including [FastMIM](https://arxiv.org/pdf/2212.06593.pdf) and [LocalMIM (CVPR23)](https://arxiv.org/abs/2303.05251).
- [TrainingAcceleration](https://github.com/huawei-noah/Efficient-Computing/tree/master/TrainingAcceleration): Accelerating neural network training via [NetworkExpansion (CVPR23)](https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Network_Expansion_for_Practical_Training_Acceleration_CVPR_2023_paper.pdf).
- [Detection](https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection): Efficient object detectors including [Gold-YOLO (NeurIPS23)](https://arxiv.org/abs/2309.11331).
- [LowLevel](https://github.com/huawei-noah/Efficient-Computing/tree/master/LowLevel): Efficient low level vision models including [IPG (CVPR24)](https://openaccess.thecvf.com/content/CVPR2024/papers/Tian_Image_Processing_GNN_Breaking_Rigidity_in_Super-Resolution_CVPR_2024_paper.pdf).