Alternatives and similar repositories for DiSparse-Multitask-Model-Compression

Users that are interested in DiSparse-Multitask-Model-Compression are comparing it to the libraries listed below

• VITA-Group / SViTE
  [NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang…
  ☆90Updated last year
• haolibai / APS-channel-search
  Revisiting Parameter Sharing for Automatic Neural Channel Number Search, NeurIPS 2020
  ☆21Updated 4 years ago
• ZiweiWangTHU / GMPQ
  This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is…
  ☆25Updated 3 years ago
• VITA-Group / UVC
  [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…
  ☆54Updated last year
• xiezheng-cs / DTQ
  PyTorch implementation of "Deep Transferring Quantization" (ECCV2020)
  ☆18Updated 3 years ago
• megvii-research / zipfls
  This repo is the official megengine implementation of the ECCV2022 paper: Efficient One Pass Self-distillation with Zipf's Label Smoothin…
  ☆26Updated 2 years ago
• megvii-research / TPS-CVPR2023
  ☆46Updated last year
• VITA-Group / WeakNAS
  [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
• GATECH-EIC / ShiftAddNAS
  [ICML 2022] ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
  ☆16Updated 3 years ago
• MingSun-Tse / Smile-Pruning
  A generic code base for neural network pruning, especially for pruning at initialization.
  ☆30Updated 2 years ago
• szq0214 / S2-BNN
  S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)
  ☆64Updated 3 years ago
• xiusu / ViTAS
  Code for ViTAS_Vision Transformer Architecture Search
  ☆50Updated 3 years ago
• MAC-AutoML / OMPQ
  ☆25Updated 3 years ago
• AIoT-MLSys-Lab / CATE
  [ICML 2021 Oral] "CATE: Computation-aware Neural Architecture Encoding with Transformers" by Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang
  ☆19Updated 4 years ago
• yuchaoli / CC
  Towards Compact CNNs via Collaborative Compression
  ☆11Updated 3 years ago
• danczs / NetworkAdjustment
  ☆13Updated 4 years ago
• MAC-AutoML / ITPruner
  ☆25Updated 3 years ago
• MingSun-Tse / TPP
  [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
  ☆33Updated 2 years ago
• bellymonster / Weighted-Soft-Label-Distillation
  ☆57Updated 4 years ago
• haolibai / Cross-Distillation
  Codes for paper "Few Shot Network Compression via Cross Distillation", AAAI 2020.
  ☆32Updated 5 years ago
• DequanWang / CoDeNet
  Algorithm-hardware Co-design for Deformable Convolution
  ☆24Updated 4 years ago
• Yu-Zhewen / SVD-NAS
  ☆11Updated 2 years ago
• xuguodong03 / UNIXKD
  ☆27Updated 2 years ago
• csyhhu / MetaQuant
  Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019
  ☆54Updated 5 years ago
• MingSun-Tse / Good-DA-in-KD
  [NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective
  ☆37Updated 2 years ago
• 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
• facebookresearch / NASViT
  code for NASViT
  ☆66Updated 3 years ago
• EkdeepSLubana / flowandprune
  Codebase for the paper "A Gradient Flow Framework for Analyzing Network Pruning"
  ☆21Updated 4 years ago
• chenbong / PSS-Net
  ☆17Updated 3 years ago
• changlin31 / AutoProg
  (CVPR 2022) Automated Progressive Learning for Efficient Training of Vision Transformers
  ☆25Updated 4 months ago