  Visual anomaly detection aims at classifying and locating the regions that
deviate from the normal appearance. Embedding-based methods and
reconstruction-based methods are two main approaches for this task. However,
they are either not efficient or not precise enough for the industrial
detection. To deal with this problem, we derive POUTA (Produce Once Utilize
Twice for Anomaly detection), which improves both the accuracy and efficiency
by reusing the discriminant information potential in the reconstructive
network. We observe that the encoder and decoder representations of the
reconstructive network are able to stand for the features of the original and
reconstructed image respectively. And the discrepancies between the symmetric
reconstructive representations provides roughly accurate anomaly information.
To refine this information, a coarse-to-fine process is proposed in POUTA,
which calibrates the semantics of each discriminative layer by the high-level
representations and supervision loss. Equipped with the above modules, POUTA is
endowed with the ability to provide a more precise anomaly location than the
prior arts. Besides, the representation reusage also enables to exclude the
feature extraction process in the discriminative network, which reduces the
parameters and improves the efficiency. Extensive experiments show that, POUTA
is superior or comparable to the prior methods with even less cost.
Furthermore, POUTA also achieves better performance than the state-of-the-art
few-shot anomaly detection methods without any special design, showing that
POUTA has strong ability to learn representations inherent in the training
data.
