Summary of Boosting Fine-grained Visual Anomaly Detection with Coarse-knowledge-aware Adversarial Learning, by Qingqing Fang et al.
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning
by Qingqing Fang, Qinliang Su, Wenxi Lv, Wenchao Xu, Jianxing Yu
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to unsupervised visual anomaly detection, which is crucial for applications such as medical imaging analysis and quality control in industries. The current methods train an auto-encoder to reconstruct normal samples and then use the reconstruction error map to detect anomalies. However, this can lead to unsatisfactory detection accuracy due to the powerful modeling abilities of neural networks. To address this issue, the authors collect a small coarsely-labeled anomaly dataset and develop a coarse-knowledge-aware adversarial learning method that aligns the distribution of reconstructed features with that of normal features. This alignment effectively suppresses the auto-encoder’s reconstruction ability on anomalies, improving detection accuracy. Furthermore, the authors propose a patch-level adversarial learning strategy to deal with anomalies that occupy small areas in images. Experimental results on six datasets demonstrate the effectiveness of their approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve unsupervised visual anomaly detection by developing a new method called coarse-knowledge-aware adversarial learning. This method helps an auto-encoder learn to detect and localize anomalies more accurately. The authors first collect some data with anomalies marked, then use this data to train the auto-encoder. They also propose another strategy for dealing with very small anomalies in images. Their approach is tested on several datasets and shown to be effective. |
Keywords
» Artificial intelligence » Alignment » Anomaly detection » Encoder » Unsupervised
