Web: http://arxiv.org/abs/2112.07662

Sept. 23, 2022, 1:15 a.m. | Niv Cohen, Ron Abutbul, Yedid Hoshen

cs.CV updates on arXiv.org arxiv.org

Out-of-distribution detection seeks to identify novelties, samples that
deviate from the norm. The task has been found to be quite challenging,
particularly in the case where the normal data distribution consists of
multiple semantic classes (e.g., multiple object categories). To overcome this
challenge, current approaches require manual labeling of the normal images
provided during training. In this work, we tackle multi-class novelty detection
without class labels. Our simple but effective solution consists of two stages:
we first discover "pseudo-class" labels …

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