Neural Architecture Search for Visual Anomaly Segmentation
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This paper presents AutoPatch, the first application of neural architecture search to the complex task of segmenting visual anomalies. Measurement of anomaly segmentation quality is challenging due to imbalanced anomaly pixels, varying region areas, and various types of anomalies. First, the weighted average precision (wAP) metric is proposed as an alternative to AUROC and AUPRO, which does not need to be limited to a specific maximum FPR. Second, a novel neural architecture search method is proposed, which enables efficient segmentation of visual anomalies without any training. By leveraging a pre-trained supernet, a black-box optimization algorithm can directly minimize FLOPS and maximize wAP on a small validation set of anomalous examples. Finally, compelling results on the widely studied MVTec [3] dataset are presented, demonstrating that AutoPatch outperforms the current state-of-the-art method PatchCore [12] with more than 18x fewer FLOPS, using only one example per anomaly type. These results highlight the potential of automated machine learning to optimize throughput in industrial quality control. The code for AutoPatch is available at: https://github.com/tommiekerssies/AutoPatch
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