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：摘要：目前最先进的无监督异常检测（UAD）方法大多依赖于对大规模数据集（例如ImageNet）预训练的冻结编码器网络进行特征表示建模。然而，从自然图像领域借用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）所需的特征重合度较低。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，并将网络定向至目标领域。我们首先采用基于特征重建的方法，通过误差检测异常。本质上，对比学习的元素被巧妙嵌入到特征重建中，以防止网络出现训练不稳定、模式崩溃和恒等捷径问题，同时同步优化目标领域的编码器和解码器。为了证明我们在不同图像领域的迁移能力，我们在两个流行的工业缺陷检测基准和三个医学图像UAD任务上进行了大量实验，结果表明我们的方法优于当前最先进技术。