Deep autoencoders provide an effective tool for learning non-linear dimensionality reduction in an unsupervised way. Recently, they have been used for the task of anomaly detection in the visual domain. By optimizing for the reconstruction error using anomaly-free examples, the common belief is that a corresponding network should fail to accurately reconstruct anomalous regions in the application phase. This goal is typically addressed by controlling the capacity of the network, either by reducing the size of the bottleneck layer or by enforcing sparsity constraints on the activations. However, neither of these techniques does explicitly penalize reconstruction of anomalous signals often resulting in poor detection. We tackle this problem by adapting a self-supervised learning regime that allows the use of discriminative information during training but focuses on the data manifold of normal examples. We emphasize that inference with our approach is very efficient during training and prediction requiring a single forward pass for each input image. Our experiments on the MVTec AD dataset demonstrate high detection and localization performance. On the texture-subset, in particular, our approach consistently outperforms recent anomaly detection methods by a significant margin.

翻译：深度自编码器为无监督学习非线性降维提供了有效工具。近年来，它们被应用于视觉领域的异常检测任务。通过使用无异常样本优化重建误差，普遍认为相应网络应在应用阶段无法准确重建异常区域。这一目标通常通过控制网络容量实现，具体方式包括缩小瓶颈层尺寸或对激活值施加稀疏性约束。然而，这两种技术均未显式惩罚异常信号的重建，常导致检测性能不佳。我们通过引入自监督学习机制解决该问题：该机制在训练阶段利用判别性信息，但聚焦于正常样本的数据流形。我们强调，所提方法在训练与预测阶段均能实现高效推理——每张输入图像仅需单次前向传播。在MVTec AD数据集上的实验表明，该方法展现出卓越的检测与定位性能。尤其值得关注的是，在纹理子集上，本方法以显著优势持续超越现有异常检测方法。