Misclassification-Aware Robust Learning from Multiple Human Labelers (Student Abstract)

Adversarial training is an effective technique for enhancing the robustness of deep neural networks (DNNs). Prior research shows that misclassified examples influence final adversarial robustness much more than correctly classified examples. Ignoring this difference during training can hurt model performance. In crowdsourcing, varying annotator expertise causes noisy, inconsistent labels. As a result, it is hard to distinguish misclassified and correctly classified examples using only provided annotations. Thus, how to use the reliability and discrepancy between these example types to improve robustness within adversarial learning remains a critical but underexplored issue. In this work, we first explore how misclassified and correctly classified examples affect learning from crowds (LFC) in adversarial environments. Then, we formulate the problem of misclassification-aware robust learning from multiple human labelers as a bilevel min-max problem. After that, we introduce MALC, a new approach to make classifiers more robust to adversarial examples via iterative adversarial example generation and parameter estimation. We conduct an extensive evaluation of the proposed MALC, showing that MALC can outperform the state-of-the-art LFC methods in both white-box and black-box settings.

Authors

• Zuoyuehe Wang Nanyang Technology University
• Chicheng Ma University of Science and Technology Beijing
• Pengpeng Chen
• Lei Chai State Key Laboratory of Complex & Critical Software Environment (CCSE), Beihang University, China Hangzhou Innovation Institute of Beihang University, China
• Yongqiang Yang
• Zhijun Chen Beihang University
• Jingzheng Li Zhongguancun Laboratory
• Bing Li
• Ting Wang City University of Hong Kong, Hong Kong SAR, China

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