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.

翻译：大多数先进的无监督异常检测方法依赖于对预训练于大规模数据集（如ImageNet）的冻结编码器网络进行特征表示建模。然而，从借用自然图像领域的编码器中提取的特征与目标异常检测领域（如工业检测和医学影像）所需的特征一致性较低。本文提出了一种新颖的认知性无监督异常检测方法——ReContrast，该方法通过优化整个网络来减少对预训练图像领域的偏差，并将网络定向至目标领域。我们以特征重建方法为基础，通过误差检测异常。本质上，对比学习元素被精巧地嵌入特征重建中，以防止网络出现训练不稳定、模式坍塌和捷径恒等映射问题，同时在同一目标领域同步优化编码器和解码器。为验证该方法在各图像领域的迁移能力，我们在两个主流工业缺陷检测基准数据集和三个医学图像无监督异常检测任务上进行了广泛实验，结果表明本方法优于当前最先进方法。