VITA-Group / SFW-Once-for-All-Pruning
[ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, Tianlong Chen, Wuyang Chen, Dong Liu, Zhangyang Wang
☆29Updated 3 years ago
Alternatives and similar repositories for SFW-Once-for-All-Pruning:
Users that are interested in SFW-Once-for-All-Pruning are comparing it to the libraries listed below
- [ICML 2022] "Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets" by Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wa…☆32Updated last year
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆32Updated last year
- ☆43Updated last year
- [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…☆52Updated last year
- [Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Prunin…☆40Updated 2 years ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆30Updated 2 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆58Updated last year
- [ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlo…☆73Updated 2 years ago
- Position-based Scaled Gradient for Model Quantization and Pruning Code (NeurIPS 2020)☆26Updated 4 years ago
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆87Updated 2 years ago
- ☆25Updated 3 years ago
- [NeurIPS‘2021] "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", Geng Yuan, Xiaolong Ma, Yanzhi Wang et al…☆18Updated 2 years ago
- Post-training sparsity-aware quantization☆34Updated last year
- Code for ICML 2022 paper "SPDY: Accurate Pruning with Speedup Guarantees"☆18Updated last year
- [ICLR'21] Neural Pruning via Growing Regularization (PyTorch)☆83Updated 3 years ago
- [ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization☆25Updated last year
- [ICML2022] Training Your Sparse Neural Network Better with Any Mask. Ajay Jaiswal, Haoyu Ma, Tianlong Chen, ying Ding, and Zhangyang Wang☆27Updated 2 years ago
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- [NeurIPS 2021] “Stronger NAS with Weaker Predictors“, Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang W…☆27Updated 2 years ago
- ☆30Updated 2 years ago
- Official PyTorch Implementation of "Learning Architectures for Binary Networks" (ECCV2020)☆26Updated 4 years ago
- [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"☆30Updated 6 months ago
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- [ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, De…☆46Updated last year
- [CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu…☆57Updated 3 years ago
- In progress.☆63Updated 10 months ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 2 years ago
- Revisiting Parameter Sharing for Automatic Neural Channel Number Search, NeurIPS 2020☆20Updated 4 years ago
- ☆17Updated 2 years ago
- official implementation of Generative Low-bitwidth Data Free Quantization(GDFQ)☆53Updated last year