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arXiv Machine Learning By Alireza Dastmalchi Saei, Shervin Rahimzadeh Arashloo

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2607. 22212v1 Announce Type: cross Abstract: Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are highly imbalanced.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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