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：大多数先进的非监督异常检测（UAD）方法依赖于对大规模数据集（如ImageNet）上预训练的冻结编码器网络进行特征建模。然而，从自然图像领域借鉴而来的编码器所提取的特征，与目标UAD领域（如工业检测和医学成像）所需特征的匹配度极低。本文提出了一种新型认知性UAD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，并将网络定向到目标领域。我们以特征重构方法为起点，通过误差来检测异常。实质上，对比学习要素被优雅地嵌入特征重构中，以防止网络出现训练不稳定、模式塌缩和恒等捷径问题，同时同步优化目标领域上的编码器和解码器。为证明我们在不同图像领域的迁移能力，我们在两个主流工业缺陷检测基准和三个医学图像UAD任务上进行了广泛实验，结果表明我们方法优于当前最先进技术。