A Unified Model for Multi-class Anomaly Detection. (arXiv:2206.03687v3 [cs.CV] UPDATED)

Despite the rapid advance of unsupervised anomaly detection, existing methods
require to train separate models for different objects. In this work, we
present UniAD that accomplishes anomaly detection for multiple classes with a
unified framework. Under such a challenging setting, popular reconstruction
networks may fall into an "identical shortcut", where both normal and anomalous
samples can be well recovered, and hence fail to spot outliers. To tackle this
obstacle, we make three improvements. First, we revisit the formulations of
fully-connected …

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