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:
this https URL

本文介绍了 AutoPatch，将神经架构搜索应用到分割视觉异常的复杂任务中。提出了加权平均精度（wAP）作为替代 AUROC 和 AUPRO 的度量标准，并提出了一种新的神经架构搜索方法，能够在没有任何训练的情况下高效地进行视觉异常分割。在 MVTec 数据集上，AutoPatch 优于当前最先进的方法 PatchCore，仅使用每个异常类型的一个样本的条件下，FLOPS 少 18 倍以上。