92 citations · 164 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning
Chonghan Chen, Haohan Wang, Leyang Hu +6
We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective.…
MRCLens: an MRC Dataset Bias Detection Toolkit
Yifan Zhong, Haohan Wang, Eric P. Xing
Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to p…
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…
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…