ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
This addresses domain-specific anomaly detection for industrial and medical applications, offering an incremental improvement over existing methods.
The paper tackled the problem of domain mismatch in unsupervised anomaly detection by proposing ReContrast, which optimizes networks for target domains like industrial inspection and medical imaging, achieving state-of-the-art results across multiple benchmarks.
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.