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INFOBATCH: LOSSLESS TRAINING SPEED UP BY UNBIASED DYNAMIC DATA PRUNING

Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training.

Data and Resources

Cite this as

Ziheng Qin, Kai Wang, Zangwei Zheng, Jianyang Gu, Xiangyu Peng, Zhaopan Xu, Daquan Zhou, Lei Shang, Baigui Sun, Xuansong Xie, Yang You (2024). Dataset: INFOBATCH: LOSSLESS TRAINING SPEED UP BY UNBIASED DYNAMIC DATA PRUNING. https://doi.org/10.57702/jcjvtj0m

DOI retrieved: December 2, 2024

Additional Info

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Created December 2, 2024
Last update December 2, 2024
Author Ziheng Qin
More Authors
Kai Wang
Zangwei Zheng
Jianyang Gu
Xiangyu Peng
Zhaopan Xu
Daquan Zhou
Lei Shang
Baigui Sun
Xuansong Xie
Yang You
Homepage https://github.com/NUS-HPC-AI-Lab/InfoBatch