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Anomaly detection without priors of the anomalies is challenging. In thefield of unsupervised anomaly detection, traditional auto-encoder (AE) tends tofail based on the assumption that by training only on normal images, the modelwill not be able to reconstruct abnormal images correctly. On the contrary, wepropose a novel patch-wise auto-encoder (Patch AE) framework, which aims atenhancing the reconstruction ability of AE to anomalies instead of weakeningit. Each patch of image is reconstructed by corresponding spatially distributedfeature vector of the learned feature representation, i.e., patch-wisereconstruction, which ensures anomaly-sensitivity of AE. Our method is simpleand efficient. It advances the state-of-the-art performances on Mvtec ADbenchmark, which proves the effectiveness of our model. It shows greatpotential in practical industrial application scenarios.

Patch-wise Auto-Encoder for Visual Anomaly Detection