Robust Out-of-distribution Detection for Neural Networks

21 Mar 2020 Jiefeng Chen Yixuan Li Xi Wu YIngyu Liang Somesh Jha

Detecting anomalous inputs is critical for safely deploying deep learning models in the real world. Existing approaches for detecting out-of-distribution (OOD) examples work well when evaluated on natural samples drawn from a sufficiently different distribution than the training data distribution... (read more)

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