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- W3132767683 abstract "There are multiple intriguing problems hovering in adversarial training, including robustness-accuracy trade-off, robust overfitting, and robustness overestimation. These problems pose great challenges to both reliable evaluation and practical deployment. Here, we show that these problems share one common cause -- low quality samples in the dataset. We first identify an intrinsic property of the data called emph{problematic score} and then design controlled experiments to investigate its connections with these problems. Specifically, we find that when problematic data is removed, robust overfitting and robustness overestimation can be largely alleviated; and robustness-accuracy trade-off becomes less significant. These observations not only verify our intuition about data quality but also open new opportunities to advance adversarial training. Interestingly, simply removing problematic data from adversarial training, while making the training set smaller, yields better robustness for leading adversarial training strategies." @default.
- W3132767683 created "2021-03-01" @default.
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- W3132767683 date "2021-02-15" @default.
- W3132767683 modified "2023-09-27" @default.
- W3132767683 title "Data Profiling for Adversarial Training: On the Ruin of Problematic Data." @default.
- W3132767683 hasPublicationYear "2021" @default.
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