Boundary Aware Learning for Out-of-distribution Detection. (arXiv:2112.11648v2 [cs.CV] UPDATED)

This paper focuses on the problem of detecting out-of-distribution (ood)
samples with neural nets. In image recognition tasks, the trained classifier
often gives high confidence score for input images which are remote from the
in-distribution (id) data, and this has greatly limited its application in real
world. For alleviating this problem, we propose a GAN based boundary aware
classifier (GBAC) for generating a closed hyperspace which only contains most
id data. Our method is based on the fact that the …

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