Post by Hannah Chen.
Our work on balanced adversarial training looks at how to train models that are robust to two different types of adversarial examples:
Hannah Chen, Yangfeng Ji, David Evans. Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models. In The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, 7-11 December 2022. [ArXiv]
Adversarial Examples At the broadest level, an adversarial example is an input crafted intentionally to confuse a model.
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