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

翻译：本文提出了AutoPatch，这是神经架构搜索首次被应用于视觉异常分割这一复杂任务中。由于异常像素分布不均、异常区域面积各异以及异常类型多样，异常分割质量的评估颇具挑战性。首先，提出了加权平均精度（wAP）指标作为AUROC和AUPRO的替代方案，该指标无需局限在特定的最大假阳性率（FPR）上。其次，提出了一种新颖的神经架构搜索方法，无需任何训练即可高效分割视觉异常。通过利用预训练的超网络，黑盒优化算法能够直接最小化浮点运算次数（FLOPS）并在小型异常样本验证集上最大化wAP。最后，在广泛研究的MVTec [3]数据集上展示了令人瞩目的结果，证明AutoPatch在仅使用每种异常类型一个样本的情况下，以超过18倍的FLOPS减少量优于当前最先进方法PatchCore [12]。这些结果凸显了自动化机器学习在优化工业质量控制吞吐量方面的潜力。AutoPatch的代码可在https://github.com/tommiekerssies/AutoPatch获取。