Anomaly Detection via Reverse Distillation from One-Class Embedding

Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The obtained compact embedding effectively preserves essential information on normal patterns, but aban-dons anomaly perturbations.

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• 1 The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
• 2 However, using similar or identical architectures to build the teacher and student models in previous studies hinders the diversity of anomalous representations.
• 3 To tackle this problem, we propose a novel T-S model consisting of a teacher encoder and a student decoder and introduce a simple yet effective “reverse distillation” paradigm accordingly.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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