  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.
