Reconstruction-based methods play an important role in unsupervised anomaly
detection in images. Ideally, we expect a perfect reconstruction for normal
samples and poor reconstruction for abnormal samples. Since the
generalizability of deep neural networks is difficult to control, existing
models such as autoencoder do not work well. In this work, we interpret the
reconstruction of an image as a divide-and-assemble procedure. Surprisingly, by
varying the granularity of division on feature maps, we are able to modulate
the reconstruction capability of the model for both normal and abnormal
samples. That is, finer granularity leads to better reconstruction, while
coarser granularity leads to poorer reconstruction. With proper granularity,
the gap between the reconstruction error of normal and abnormal samples can be
maximized. The divide-and-assemble framework is implemented by embedding a
novel multi-scale block-wise memory module into an autoencoder network.
Besides, we introduce adversarial learning and explore the semantic latent
representation of the discriminator, which improves the detection of subtle
anomaly. We achieve state-of-the-art performance on the challenging MVTec AD
dataset. Remarkably, we improve the vanilla autoencoder model by 10.1% in terms
of the AUROC score.

本文提出了一种自动编码器框架中的分割 - 组装策略，并引入了多尺度块状记忆模块和对抗学习以改进无监督图像异常检测的性能。