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Anomaly detection aims to separate anomalies from normal samples, and thepretrained network is promising for anomaly detection. However, adapting thepretrained features would be confronted with the risk of pattern collapse whenfinetuning on one-class training data. In this paper, we propose an anomalydetection framework called constrained adaptive projection with pretrainedfeatures (CAP). Combined with pretrained features, a simple linear projectionhead applied on a specific input and its k most similar pretrained normalrepresentations is designed for feature adaptation, and a reformedself-attention is leveraged to mine the inner-relationship among one-classsemantic features. A loss function is proposed to avoid potential patterncollapse. Concretely, it considers the similarity between a specific data andits corresponding adaptive normal representation, and incorporates a constraintterm slightly aligning pretrained and adaptive spaces. Our method achievesstate-ofthe-art anomaly detection performance on semantic anomaly detection andsensory anomaly detection benchmarks including 96.5% AUROC on CIFAR- 100dataset, 97.0% AUROC on CIFAR-10 dataset and 89.9% AUROC on MvTec dataset.

Constrained Adaptive Projection with Pretrained Features for Anomaly Detection