Efficient Computing

This repo is a collection of Efficient-Computing methods developed by Huawei Noah’s Ark Lab.

• Data-Efficient-Model-Compression is a series of compression methods with no or little training data.
• BinaryNetworks: Binary neural networks including AdaBin (ECCV22).
• Distillation: Knowledge distillation methods including ManifoldKD (NeurIPS22) and VanillaKD (NeurIPS23).
• Pruning: Network pruning methods including GAN-pruning (ICCV19), SCOP (NeurIPS20), ManiDP (CVPR21), and RPG (NeurIPS23).
• Quantization: Model quantization methods including DynamicQuant (CVPR22).
• Self-supervised: self-supervised learning including FastMIM and LocalMIM (CVPR23).
• TrainingAcceleration: Accelerating neural network training via NetworkExpansion (CVPR23).
• Detection: Efficient object detectors including Gold-YOLO (NeurIPS23).
• LowLevel: Efficient low level vision models including IPG (CVPR24).