Alternatives and similar repositories for DFPC-Pruning

Users that are interested in DFPC-Pruning are comparing it to the libraries listed below

• MingSun-Tse / TPP
  [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
  ☆33Updated 2 years ago
• diaoenmao / Pruning-Deep-Neural-Networks-from-a-Sparsity-Perspective
  [ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective
  ☆24Updated last year
• zejiangh / Filter-GaP
  The official PyTorch implementation of CHEX: CHannel EXploration for CNN Model Compression (CVPR 2022). Paper is available at https://ope…
  ☆38Updated 2 years ago
• htqin / DSG
  This project is the official implementation of our accepted IEEE TPAMI paper Diverse Sample Generation: Pushing the Limit of Data-free Qu…
  ☆14Updated 2 years ago
• xidongwu / AutoTrainOnce
  ☆17Updated 8 months ago
• jaeho-lee / layer-adaptive-sparsity
  In progress.
  ☆64Updated last year
• lliai / EMQ-series
  [ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
  ☆26Updated last year
• VITA-Group / Random_Pruning
  [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
• Akimoto-Cris / RD_PRUNE
  [ICCV 2023] Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
  ☆24Updated last year
• hustvl / PD-Quant
  [CVPR 2023] PD-Quant: Post-Training Quantization Based on Prediction Difference Metric
  ☆56Updated 2 years ago
• xuke225 / EQ-Net
  EQ-Net [ICCV 2023]
  ☆29Updated last year
• hikvision-research / SAViT
  ☆12Updated last year
• shawnricecake / search-llm
  [NeurIPS 2024] Search for Efficient LLMs
  ☆14Updated 5 months ago
• HuangOwen / QAT-ACS
  [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"
  ☆33Updated 10 months ago
• zyxxmu / Bi-Mask
  Pytorch implementation of our paper accepted by ICML 2023 -- "Bi-directional Masks for Efficient N:M Sparse Training"
  ☆12Updated 2 years ago
• htqin / BiBench
  [ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binar…
  ☆56Updated last year
• nbasyl / OFQ
  The official implementation of the ICML 2023 paper OFQ-ViT
  ☆31Updated last year
• lihuantong / HAST
  ☆12Updated last year
• lliai / DisWOT-CVPR2023
  ☆26Updated last year
• MingSun-Tse / Why-the-State-of-Pruning-so-Confusing
  [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
• HuangOwen / Quantization-Variation
  [TMLR] Official PyTorch implementation of paper "Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precisio…
  ☆45Updated 9 months ago
• zhexinli / Q-ViT-DeiT
  DeiT implementation for Q-ViT
  ☆25Updated 2 months ago
• zysxmu / IntraQ
  Pytorch implementation of our paper accepted by CVPR 2022 -- IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Sh…
  ☆33Updated 3 years ago
• Yanqi-Chen / LATS
  To appear in the 11th International Conference on Learning Representations (ICLR 2023).
  ☆17Updated 2 years ago
• zysxmu / FDDA
  Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization
  ☆15Updated 2 years ago
• yaozhewei / HAP
  ☆43Updated last year
• OpenGVLab / LLMPrune-BESA
  BESA is a differentiable weight pruning technique for large language models.
  ☆17Updated last year
• wimh966 / QDrop
  The official PyTorch implementation of the ICLR2022 paper, QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quan…
  ☆122Updated last year
• MAC-AutoML / OMPQ
  ☆25Updated 3 years ago
• YanjingLi0202 / Q-ViT
  The official implementation of the NeurIPS 2022 paper Q-ViT.
  ☆93Updated 2 years ago