90 citations · 248 across the 15 of their papers we have counts for
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cs.CV2022
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Minh-Long Luu, Zeyi Huang, Eric P. Xing +2
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks. Over the years, the research community expands mix-up method…
cs.LG2022
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Haohan Wang, Zeyi Huang, Xindi Wu +1
Data augmentation has been proven to be an effective technique for developing machine learning models that are robust to known classes of distributional shifts (e.g., rotations of…
cs.LG2022★ 1 cited
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Zeyi Huang, Haohan Wang, Dong Huang +2
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in t…