Anomaly detection without priors of the anomalies is challenging. In the
field of unsupervised anomaly detection, traditional auto-encoder (AE) tends to
fail based on the assumption that by training only on normal images, the model
will not be able to reconstruct abnormal images correctly. On the contrary, we
propose a novel patch-wise auto-encoder (Patch AE) framework, which aims at
enhancing the reconstruction ability of AE to anomalies instead of weakening
it. Each patch of image is reconstructed by corresponding spatially distributed
feature vector of the learned feature representation, i.e., patch-wise
reconstruction, which ensures anomaly-sensitivity of AE. Our method is simple
and efficient. It advances the state-of-the-art performances on Mvtec AD
benchmark, which proves the effectiveness of our model. It shows great
potential in practical industrial application scenarios.

无先验异常检测是具有挑战性的。我们提出了一种新型的分块自编码器（Patch AE）框架，旨在增强自编码器对异常图像的重构能力而不是削弱。通过对学习到的特征表示的空间分布特征向量的相应位置进行重构，即分块重构，确保了自编码器对异常的敏感性。我们的方法简单高效，在 Mvtec AD 基准方面取得了最新的研究成果，证明了我们模型的有效性。它在实际工业应用场景中显示出巨大的潜力。