11 citations · 22 across the 4 of their papers we have counts for
4 papers
Dataset Quantization
Daquan Zhou, Kai Wang, Jianyang Gu +5
State-of-the-art deep neural networks are trained with large amounts (millions or even billions) of data. The expensive computation and memory costs make it difficult to train them…
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
Ziheng Qin, Kai Wang, Zangwei Zheng +8
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. This could lead to…
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
Kai Wang, Xiangyu Peng, Shuo Yang +4
Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to…
Crafting Better Contrastive Views for Siamese Representation Learning
Xiangyu Peng, Kai Wang, Zheng Zhu +2
Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siames…