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Official code for our ECCV'22 paper "A Fast Knowledge Distillation Framework for Visual Recognition"

X3d Multigrid ⭐ 45

PyTorch implementation of X3D models with Multigrid training.

Efficienttrain ⭐ 38

Reducing the training cost of SOTA deep networks (e.g., ConvNeXts, DeiT, PVT, Swin/CSWin, MAE) by >1.5× on ImageNet-1K/22K without sacrificing the accuracy.

Datasetculling ⭐ 27

✂️ Dataset Culling: Faster training of domain specific models with distillation ✂️ (IEEE ICIP 2019)

[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

Mlpinit For Gnns ⭐ 17

[ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization

Fractrain ⭐ 11

[NeurIPS 2020] "FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training" by Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin

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