Towards training noise-robust anomaly detection via collaborative adversarial flows

Abstract

Unsupervised visual anomaly detection (UVAD) aims at identifying unobserved anomalies by training a classification model with anomaly-free images, which has been widespread applied to surface inspection of multiple industrial products. However, previous UVAD methods trained by ideal normal samples suffer from performance degradation in real world industry because of the inevitable intrusion of noisy samples into the training set. Addressing this issue, this article introduces a novel strategy, Collaborative Adversarial Flows (CAF), which employs multiple normalizing flows to efficiently filter out noisy samples and accurately construct distributions of normal samples. Unlike previous methods that focused solely on learning accurate representations of normal samples, CAF adds an additional learning objective of inferring noisy samples based on distributional metrics while optimizing representations. Furthermore, CAF establishes a flow-based collaborative adversarial paradigm that promotes high-likelihood samples and rejects low-likelihood noisy samples in optimizing the latent distribution, thereby enhancing the robustness of model under contaminated training set. Comprehensive experiments on the public benchmark MVTec AD under human-made noise intrusion demonstrate that CAF effectively improves the robustness to noisy samples and achieves impressive performance. In electronic manufacturing processes with real industrial noise, the applicability and robustness of CAF have been further proved by the inspection of surface mount devices.