Out-of-Distribution Detection Using Layerwise Uncertainty in Deep Neural Networks

ICLR 2020 Hirono OkamotoMasahiro SuzukiYutaka Matsuo

In this paper, we tackle the problem of detecting samples that are not drawn from the training distribution, i.e., out-of-distribution (OOD) samples, in classification. Many previous studies have attempted to solve this problem by regarding samples with low classification confidence as OOD examples using deep neural networks (DNNs)... (read more)

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