Toward Metrics for Differentiating Out-of-Distribution Sets

18 Oct 2019Mahdieh AbbasiChangjian ShuiArezoo RajabiChristian GagneRakesh Bobba

Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-of-distribution (OOD) samples nearly as confidently as in-distribution samples. To tackle this challenge, some recent works have demonstrated the gains of leveraging available OOD sets for training end-to-end calibrated CNNs... (read more)

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