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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.

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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

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Additional Info

Field	Value
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