Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous." In this paper we present results demonstrating that this intuition surprisingly seems not to extend to deep AD on images. For a recent AD benchmark on ImageNet, classifiers trained to discern between normal samples and just a few (64) random natural images are able to outperform the current state of the art in deep AD. Experimentally we discover that the multiscale structure of image data makes example anomalies exceptionally informative.

翻译：虽然异常检测（AD）可被视为分类问题（正常vs异常），但由于通常无法获取或难以利用能够充分表征"异常"含义的数据集，因此通常以无监督方式进行处理。本文的研究结果表明，这一直觉认知在图像深度异常检测中似乎并不成立。在ImageNet上进行的近期AD基准测试中，仅使用少量（64张）随机自然图像作为异常样本训练的分类器，其区分正常样本与异常样本的表现已超越当前深度AD技术的最优水平。通过实验我们发现，图像数据的多尺度结构使得异常样本具有异常丰富的信息量。