38 citations · 42 across the 12 of their papers we have counts for
3 papers · 1 filter
Conflict-Aware Adversarial Training
Zhiyu Xue, Haohan Wang, Yao Qin +1
Adversarial training is the most effective method to obtain adversarial robustness for deep neural networks by directly involving adversarial samples in the training procedure. To…
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
Jingfeng Zhang, Bo Song, Haohan Wang +4
Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed diff…
A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets
Esla Timothy Anzaku, Haohan Wang, Arnout Van Messem +1
Deep Neural Network (DNN) models are increasingly evaluated using new replication test datasets, which have been carefully created to be similar to older and popular benchmark data…