  Visual anomaly detection is commonly used in industrial quality inspection.
In this paper, we present a new dataset as well as a new self-supervised
learning method for ImageNet pre-training to improve anomaly detection and
segmentation in 1-class and 2-class 5/10/high-shot training setups. We release
the Visual Anomaly (VisA) Dataset consisting of 10,821 high-resolution color
images (9,621 normal and 1,200 anomalous samples) covering 12 objects in 3
domains, making it the largest industrial anomaly detection dataset to date.
Both image and pixel-level labels are provided. We also propose a new
self-supervised framework - SPot-the-difference (SPD) - which can regularize
contrastive self-supervised pre-training, such as SimSiam, MoCo and SimCLR, to
be more suitable for anomaly detection tasks. Our experiments on VisA and
MVTec-AD dataset show that SPD consistently improves these contrastive
pre-training baselines and even the supervised pre-training. For example, SPD
improves Area Under the Precision-Recall curve (AU-PR) for anomaly segmentation
by 5.9% and 6.8% over SimSiam and supervised pre-training respectively in the
2-class high-shot regime. We open-source the project at
http://github.com/amazon-research/spot-diff .
