Spatial Contrastive Learning for Anomaly Detection and Localization
Dae-Hwan Kim, Daun Jeong, Hyungmin Kim, Kibong Chong, Seungryong Kim, Hansang Cho
Abstract
With the development of deep learning, abnormal detection methods have been widely presented to improve performances in various applications, including visual inspection systems. However, there remains difficult to be directly applied to real-world applications, which often include the lack of abnormal samples and diversity. This paper proposes contra embedding that adopts progressive autoencoder with contrastive learning to address these difficulties. The autoencoder is trained progressively to reproduce the details of the original images, and modified CutPaste augmentation helps to learn to recover normal images. Especially, contrastive learning based on normal embedding vectors effectively reduces false positives caused by the autoencoder. The proposed method is also helpful when normal data have complex shapes, sizes, and colors. In experiments, MVTec AD dataset is used to show the generalization ability of the proposed method in various real-world applications. It achieves over 98.0% AUROCs in detection and 97.7% AUROCs in the localization, respectively, without using the ImageNet pre-trained model as in previous methods.