LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
This work addresses the problem of detecting anomalies in flexible manufacturing systems for industries needing efficient quality control, though it is incremental as it builds on existing generative approaches.
The paper tackles unsupervised multi-class anomaly detection by proposing LafitE, a latent diffusion model with feature editing, which significantly outperforms state-of-the-art methods on benchmark datasets like MVTec-AD and MPDD, achieving higher average AUROC scores.
In the context of flexible manufacturing systems that are required to produce different types and quantities of products with minimal reconfiguration, this paper addresses the problem of unsupervised multi-class anomaly detection: develop a unified model to detect anomalies from objects belonging to multiple classes when only normal data is accessible. We first explore the generative-based approach and investigate latent diffusion models for reconstruction to mitigate the notorious ``identity shortcut'' issue in auto-encoder based methods. We then introduce a feature editing strategy that modifies the input feature space of the diffusion model to further alleviate ``identity shortcuts'' and meanwhile improve the reconstruction quality of normal regions, leading to fewer false positive predictions. Moreover, we are the first who pose the problem of hyperparameter selection in unsupervised anomaly detection, and propose a solution of synthesizing anomaly data for a pseudo validation set to address this problem. Extensive experiments on benchmark datasets MVTec-AD and MPDD show that the proposed LafitE, \ie, Latent Diffusion Model with Feature Editing, outperforms state-of-art methods by a significant margin in terms of average AUROC. The hyperparamters selected via our pseudo validation set are well-matched to the real test set.