SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

• URL: http://arxiv.org/abs/2207.14315v1
• Date: Thu, 28 Jul 2022 18:00:03 GMT
• Title: SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
• Authors: Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer
• Abstract summary: 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. We propose a new self-supervised framework - SPot-the-difference (SPD) - which can regularize contrastive self-supervised pre-training. Experiments on VisA and MVTec-AD dataset show that SPD consistently improves contrastive pre-training baselines and even the supervised pre-training.
• Score: 17.954335118363964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 .

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