Most advanced unsupervised anomaly detection (UAD) methods rely on modeling
feature representations of frozen encoder networks pre-trained on large-scale
datasets, e.g. ImageNet. However, the features extracted from the encoders that
are borrowed from natural image domains coincide little with the features
required in the target UAD domain, such as industrial inspection and medical
imaging. In this paper, we propose a novel epistemic UAD method, namely
ReContrast, which optimizes the entire network to reduce biases towards the
pre-trained image domain and orients the network in the target domain. We start
with a feature reconstruction approach that detects anomalies from errors.
Essentially, the elements of contrastive learning are elegantly embedded in
feature reconstruction to prevent the network from training instability,
pattern collapse, and identical shortcut, while simultaneously optimizing both
the encoder and decoder on the target domain. To demonstrate our transfer
ability on various image domains, we conduct extensive experiments across two
popular industrial defect detection benchmarks and three medical image UAD
tasks, which shows our superiority over current state-of-the-art methods.Comment: NeurIPS 2023 Poste