  In this paper, we propose a framework called TrustMAE to address the problem
of product defect classification. Instead of relying on defective images that
are difficult to collect and laborious to label, our framework can accept
datasets with unlabeled images. Moreover, unlike most anomaly detection
methods, our approach is robust against noises, or defective images, in the
training dataset. Our framework uses a memory-augmented auto-encoder with a
sparse memory addressing scheme to avoid over-generalizing the auto-encoder,
and a novel trust-region memory updating scheme to keep the noises away from
the memory slots. The result is a framework that can reconstruct defect-free
images and identify the defective regions using a perceptual distance network.
When compared against various state-of-the-art baselines, our approach performs
competitively under noise-free MVTec datasets. More importantly, it remains
effective at a noise level up to 40% while significantly outperforming other
baselines.
