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Probabilistic Margins for Instance Reweighting in Adversarial Training
qizhou wang · Feng Liu · Bo Han · Tongliang Liu · Chen Gong · Gang Niu · Mingyuan Zhou · Masashi Sugiyama

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights. However, existing methods measuring the closeness are not very reliable: they are discrete and can take only a few values, and they are path-dependent, i.e., they may change given the same start and end points with different attack paths. In this paper, we propose three types of probabilistic margin (PM), which are continuous and path-independent, for measuring the aforementioned closeness and reweighing adversarial data. Specifically, a PM is defined as the difference between two estimated class-posterior probabilities, e.g., such a probability of the true label minus the probability of the most confusing label given some natural data. Though different PMs capture different geometric properties, all three PMs share a negative correlation with the vulnerability of data: data with larger/smaller PMs are safer/riskier and should have smaller/larger weights. Experiments demonstrated that PMs are reliable and PM-based reweighting methods outperformed state-of-the-art counterparts.

Author Information

qizhou wang (Hong Kong Baptist University)
Feng Liu (University of Technology Sydney)
Tongliang Liu (The University of Sydney)
Chen Gong (Nanjing University of Science and Technology)
Gang Niu (RIKEN)
Gang Niu

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.

Mingyuan Zhou (University of Texas at Austin)
Masashi Sugiyama (RIKEN / University of Tokyo)

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