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A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
April 3, 2024, 4:42 a.m. | Galadrielle Humblot-Renaux, Sergio Escalera, Thomas B. Moeslund
cs.LG updates on arXiv.org arxiv.org
Abstract: The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification, the task of detecting images outside of a model's training domain is known as out-of-distribution (OOD) detection. While there has been a growing research interest in developing post-hoc OOD detection methods, there has been comparably little discussion around how these methods perform when the underlying classifier is not trained on a clean, carefully …
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