  This paper presents the first application of neural architecture search to
the complex task of segmenting visual anomalies. Measurement of anomaly
segmentation performance is challenging due to imbalanced anomaly pixels,
varying region areas, and various types of anomalies. First, the
region-weighted Average Precision (rwAP) metric is proposed as an alternative
to existing metrics, which does not need to be limited to a specific maximum
false positive rate. Second, the AutoPatch 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 computational complexity and maximize
performance on a small validation set of anomalous examples. Finally,
compelling results are presented on the widely studied MVTec dataset,
demonstrating that AutoPatch outperforms the current state-of-the-art with
lower computational complexity, using only one example per type of anomaly. The
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
