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Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection. (arXiv:2209.07837v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
Out-of-distribution (OOD) detection is the key to deploying models safely in
the open world. For OOD detection, collecting sufficient in-distribution (ID)
labeled data is usually more time-consuming and costly than unlabeled data.
When ID labeled data is limited, the previous OOD detection methods are no
longer superior due to their high dependence on the amount of ID labeled data.
Based on limited ID labeled data and sufficient unlabeled data, we define a new
setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD). To …