ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining

TL;DR

This paper introduces ADPretrain, a novel pretraining framework that learns specialized, robust representations for industrial anomaly detection by focusing on discrepancies between normal and abnormal features, outperforming traditional ImageNet-based methods.

Contribution

The authors propose a new AD-specific pretraining method using angle- and norm-oriented contrastive losses on a large-scale AD dataset, improving anomaly detection performance.

Findings

Pretrained features outperform ImageNet-based features across multiple datasets.

The method improves anomaly detection accuracy with various backbone networks.

Extensive experiments validate the effectiveness of ADPretrain.

Abstract

The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pretraining. However, regardless of supervised or self-supervised pretraining, the pretraining process on ImageNet does not match the goal of anomaly detection (i.e., pretraining in natural images doesn't aim to distinguish between normal and abnormal). Moreover, natural images and industrial image data in AD scenarios typically have the distribution shift. The two issues can cause ImageNet-pretrained features to be suboptimal for AD tasks. To further promote the development of the AD field, pretrained representations specially for AD tasks are eager and very valuable. To this end, we propose a novel AD representation learning framework specially designed for learning robust and discriminative pretrained representations for industrial anomaly detection. Specifically, closely surrounding the goal of anomaly detection (i.e., focus on discrepancies between normals and anomalies), we propose angle- and norm-oriented contrastive losses to maximize the angle size and norm difference between normal and abnormal features simultaneously. To avoid the distribution shift from natural images to AD images, our pretraining is performed on a large-scale AD dataset, RealIAD. To further alleviate the potential shift between pretraining data and downstream AD datasets, we learn the pretrained AD representations based on the class-generalizable representation, residual features. For evaluation, based on five embedding-based AD methods, we simply replace their original features with our pretrained representations. Extensive experiments on five AD datasets and five backbones consistently show the superiority of our pretrained features. The code is available at https://github.com/xcyao00/ADPretrain.