Attention-Guided Perturbation for Unsupervised Image Anomaly Detection

• URL: http://arxiv.org/abs/2408.07490v1
• Date: Wed, 14 Aug 2024 12:12:43 GMT
• Title: Attention-Guided Perturbation for Unsupervised Image Anomaly Detection
• Authors: Tingfeng Huang, Yuxuan Cheng, Jingbo Xia, Rui Yu, Yuxuan Cai, Jinhai Xiang, Xinwei He, Xiang Bai,
• Abstract summary: We present a reconstruction framework named Attention-Guided Pertuation Network (AGPNet) It learns to add perturbation noise with an attention mask, for accurate unsupervised anomaly detection. Our framework obtains leading anomaly detection performance under various setups including few-shot, one-class, and multi-class setups.
• Score: 39.48326211958042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstruction-based methods have significantly advanced modern unsupervised anomaly detection. However, the strong capacity of neural networks often violates the underlying assumptions by reconstructing abnormal samples well. To alleviate this issue, we present a simple yet effective reconstruction framework named Attention-Guided Pertuation Network (AGPNet), which learns to add perturbation noise with an attention mask, for accurate unsupervised anomaly detection. Specifically, it consists of two branches, \ie, a plain reconstruction branch and an auxiliary attention-based perturbation branch. The reconstruction branch is simply a plain reconstruction network that learns to reconstruct normal samples, while the auxiliary branch aims to produce attention masks to guide the noise perturbation process for normal samples from easy to hard. By doing so, we are expecting to synthesize hard yet more informative anomalies for training, which enable the reconstruction branch to learn important inherent normal patterns both comprehensively and efficiently. Extensive experiments are conducted on three popular benchmarks covering MVTec-AD, VisA, and MVTec-3D, and show that our framework obtains leading anomaly detection performance under various setups including few-shot, one-class, and multi-class setups.

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