INCHEON-CHO / Dynamic_Model_Pruning_with_Feedback
Implement of Dynamic Model Pruning with Feedback with pytorch
☆40Updated 2 years ago
Alternatives and similar repositories for Dynamic_Model_Pruning_with_Feedback:
Users that are interested in Dynamic_Model_Pruning_with_Feedback are comparing it to the libraries listed below
- In progress.☆63Updated last year
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆89Updated 2 years ago
- [ICLR'21] Neural Pruning via Growing Regularization (PyTorch)☆83Updated 3 years ago
- Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.☆107Updated 5 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆31Updated 3 years ago
- The PyTorch implementation of Learned Step size Quantization (LSQ) in ICLR2020 (unofficial)☆131Updated 4 years ago
- An official implementation of "Network Quantization with Element-wise Gradient Scaling" (CVPR 2021) in PyTorch.☆91Updated last year
- ☆39Updated 2 years ago
- Any-Precision Deep Neural Networks (AAAI 2021)☆60Updated 4 years ago
- a pytorch implement of mobileNet v2 on cifar10☆62Updated 2 years ago
- Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).☆34Updated 2 years ago
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- A PyTorch Implementation of Feature Boosting and Suppression☆18Updated 4 years ago
- ☆43Updated last year
- ☆12Updated last year
- Pytorch implementation of our paper accepted by ICCV 2021 -- ReCU: Reviving the Dead Weights in Binary Neural Networks http://arxiv.org/a…☆38Updated 3 years ago
- ☆30Updated 3 years ago
- BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization (ICLR 2021)☆40Updated 4 years ago
- SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY☆113Updated 5 years ago
- Implementation network trimming using pytorch☆13Updated 4 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- [NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang…☆89Updated last year
- Position-based Scaled Gradient for Model Quantization and Pruning Code (NeurIPS 2020)☆26Updated 4 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
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆33Updated last year
- A pytorch implementation of DoReFa-Net☆134Updated 5 years ago
- Post-training sparsity-aware quantization☆34Updated 2 years ago
- This project is the official implementation of our accepted ICLR 2022 paper BiBERT: Accurate Fully Binarized BERT.☆88Updated last year
- A pytorch implementation of dorefa quantization☆113Updated 5 years ago
- official implementation of Generative Low-bitwidth Data Free Quantization(GDFQ)☆54Updated last year