Detecting Overfitting via Adversarial Examples

NeurIPS 2019 Roman WerpachowskiAndrás GyörgyCsaba Szepesvári

The repeated community-wide reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test-error rates. Verifying whether a learned model is overfitted to a test set is challenging as independent test sets drawn from the same data distribution are usually unavailable, while other test sets may introduce a distribution shift... (read more)

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