diaoenmao / Pruning-Deep-Neural-Networks-from-a-Sparsity-PerspectiveLinks
[ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective
☆25Updated last year
Alternatives and similar repositories for Pruning-Deep-Neural-Networks-from-a-Sparsity-Perspective
Users that are interested in Pruning-Deep-Neural-Networks-from-a-Sparsity-Perspective are comparing it to the libraries listed below
Sorting:
- Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).☆39Updated 3 years ago
- [ICLR 2023] PyTorch code for DFPC: Data flow driven pruning of coupled channels without data.☆14Updated 2 years ago
- Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization☆15Updated 3 years ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆31Updated 3 years ago
- Pytorch implementation of our paper accepted by IEEE TNNLS, 2022 — Carrying out CNN Channel Pruning in a White Box☆18Updated 3 years ago
- To appear in the 11th International Conference on Learning Representations (ICLR 2023).☆18Updated 2 years ago
- Code for ICCV23 paper "Automatic network pruning via Hilbert Schmidt independence criterion lasso under information bottleneck principle"☆18Updated 2 years ago
- In progress.☆65Updated last year
- ☆37Updated 3 years ago
- A reproduction of PRUNING FILTERS FOR EFFICIENT CONVNETS☆28Updated 5 years ago
- The official PyTorch implementation of CHEX: CHannel EXploration for CNN Model Compression (CVPR 2022). Paper is available at https://ope…☆38Updated 3 years ago
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆34Updated 2 years ago
- EQ-Net [ICCV 2023]☆30Updated 2 years ago
- Recent Advances on Efficient Vision Transformers☆53Updated 2 years ago
- ☆26Updated 3 years ago
- [ICLR'21] Neural Pruning via Growing Regularization (PyTorch)☆83Updated 4 years ago
- Code for ICML 2022 paper "SPDY: Accurate Pruning with Speedup Guarantees"☆20Updated 2 years ago
- [ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization☆28Updated last year
- Pytorch implementation of our paper accepted by CVPR 2022 -- IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Sh…☆33Updated 3 years ago
- The official implementation of paper PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search☆30Updated 2 years ago
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆32Updated 3 years ago
- [ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, De…☆45Updated last year
- Awesome Pruning. ✅ Curated Resources for Neural Network Pruning.☆169Updated last year
- ☆26Updated last year
- [ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlo…☆76Updated 2 years ago
- [ICCV 2023] Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks☆25Updated last year
- ☆17Updated 11 months ago
- [ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binar…☆56Updated last year
- ☆22Updated last year
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago