{"id":15029844,"url":"https://github.com/huawei-noah/efficient-computing","last_synced_at":"2025-05-14T15:07:55.326Z","repository":{"id":45899448,"uuid":"206295259","full_name":"huawei-noah/Efficient-Computing","owner":"huawei-noah","description":"Efficient computing methods developed by Huawei Noah's Ark Lab","archived":false,"fork":false,"pushed_at":"2024-11-05T13:53:24.000Z","size":105039,"stargazers_count":1257,"open_issues_count":20,"forks_count":218,"subscribers_count":23,"default_branch":"master","last_synced_at":"2025-04-04T23:02:13.462Z","etag":null,"topics":["binary-neural-networks","knowledge-distillation","model-compression","pruning","quantization","self-supervised"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","readme":"# Efficient Computing\r\n\r\nThis repo is a collection of Efficient-Computing methods developed by Huawei Noah's Ark Lab. \r\n\r\n- [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.\r\n- [BinaryNetworks](https://github.com/huawei-noah/Efficient-Computing/tree/master/BinaryNetworks): Binary neural networks including [AdaBin (ECCV22)](https://arxiv.org/abs/2208.08084).\r\n- [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).\r\n- [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).\r\n- [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).\r\n- [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).\r\n- [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).\r\n- [Detection](https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection): Efficient object detectors including [Gold-YOLO (NeurIPS23)](https://arxiv.org/abs/2309.11331).\r\n- [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).\r\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuawei-noah%2Fefficient-computing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhuawei-noah%2Fefficient-computing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuawei-noah%2Fefficient-computing/lists"}