Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks. (arXiv:2111.12379v2 [cs.CV] UPDATED)

Anomaly detection is important in many real-life applications. Recently,
self-supervised learning has greatly helped deep anomaly detection by
recognizing several geometric transformations. However these methods lack finer
features, usually highly depend on the anomaly type, and do not perform well on
fine-grained problems. To address these issues, we first introduce in this work
three novel and efficient discriminative and generative tasks which have
complementary strength: (i) a piece-wise jigsaw puzzle task focuses on
structure cues; (ii) a tint rotation recognition …

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