Neural Architecture Search for Visual Anomaly Segmentation

TL;DR

This paper introduces AutoPatch, a neural architecture search method that efficiently segments visual anomalies without training, using a new metric and outperforming state-of-the-art methods on the MVTec dataset.

Contribution

It proposes AutoPatch, a novel neural architecture search approach that optimizes anomaly segmentation performance with minimal data and computational complexity.

Findings

AutoPatch outperforms current state-of-the-art methods.

It achieves high segmentation accuracy with only one example per anomaly type.

The method reduces computational complexity compared to existing techniques.

Abstract

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