great8nctu / FOX-NASLinks
FOX-NAS: Fast, On-device and Explainable NeuralArchitecture Search
☆11Updated 3 years ago
Alternatives and similar repositories for FOX-NAS
Users that are interested in FOX-NAS are comparing it to the libraries listed below
Sorting:
- This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is…☆25Updated 3 years ago
- [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"☆33Updated 10 months ago
- Official PyTorch implementation of "Evolving Search Space for Neural Architecture Search"☆12Updated 3 years ago
- Arch-Net: Model Distillation for Architecture Agnostic Model Deployment☆22Updated 3 years ago
- ☆28Updated 4 years ago
- [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
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆33Updated 2 years ago
- Revisiting Parameter Sharing for Automatic Neural Channel Number Search, NeurIPS 2020☆21Updated 4 years ago
- An Tensorflow.keras implementation of Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorizatio…☆10Updated 5 years ago
- Pytorch implementation of our paper (TNNLS) -- Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters☆12Updated 3 years ago
- Pytorch implementation of TPAMI 2022 -- 1xN Pattern for Pruning Convolutional Neural Networks☆43Updated 2 years ago
- [ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization☆26Updated last year
- NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021☆37Updated 3 years ago
- BESA is a differentiable weight pruning technique for large language models.☆17Updated last year
- [ICLR 2021] CompOFA: Compound Once-For-All Networks For Faster Multi-Platform Deployment☆24Updated 2 years ago
- Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019☆54Updated 5 years ago
- ☆20Updated 2 years ago
- [ICML 2022] ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks☆16Updated 3 years ago
- This is the official repo for "Differentiable Model Scaling using Differentiable Topk"☆11Updated last year
- Pytorch implementation of our paper accepted by IEEE TNNLS, 2021 -- Network Pruning using Adaptive Exemplar Filters☆22Updated 4 years ago
- ☆10Updated 4 years ago
- ☆35Updated 5 years ago
- Collections of model quantization algorithms. Any issues, please contact Peng Chen (blueardour@gmail.com)☆71Updated 3 years ago
- Official code of "NAS acceleration via proxy data", IJCAI21☆10Updated 3 years ago
- PyTorch implementation of "Deep Transferring Quantization" (ECCV2020)☆18Updated 3 years ago
- Official PyTorch Implementation of "Learning Architectures for Binary Networks" (ECCV2020)☆26Updated 4 years ago
- Paper collection about model compression and acceleration: Pruning, Quantization, Knowledge Distillation, Low Rank Factorization, etc☆25Updated 4 years ago
- [ICML 2022] "DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks", by Yonggan …☆71Updated 2 years ago
- Towards Compact CNNs via Collaborative Compression☆11Updated 3 years ago
- ☆11Updated 2 years ago