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.
Highlights
Huge performance improvement has been achieved in the unsupervised anomaly detection task, which aims to detect unusual events of test data by only training unlabeled data [1], [2]
This paper proposes a novel method called contra embedding for unsupervised anomaly detection trained on normal data, including various characteristics such as shape, size, and color
The method improves the reconstruction performance of the autoencoder by using progressive learning and modified CutPaste augmentation
Summary
Huge performance improvement has been achieved in the unsupervised anomaly detection task, which aims to detect unusual events of test data by only training unlabeled data [1], [2]. Embedding similarity-based methods in [13], [14], [15], [1] showed good performance using feature vectors of a pre-trained network to detect the abnormality. Structural Similarity Index (SSIM) from autoencoder detects abnormalities effectively but detects high-frequency regions of normal images as defects This problem is supplemented with a generated mask through contrastive learning. To improve the quality of reconstruction images, SSIMAE [7], [8] propose to train an autoencoder with the structural similarity loss for comparing luminance, contrast and structural information between local image regions.
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