Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton

TL;DR

The paper proposes FUTUREG, a novel framework with feature purification and guided decoding modules that improve anomaly detection accuracy in multi-class OOD and industrial scenarios by enhancing reconstruction quality.

Contribution

Introducing FUTUREG, which combines feature purification and cross-level feature guiding to improve anomaly detection in multi-class datasets.

Findings

Achieves state-of-the-art results in multi-class OOD detection.

Maintains competitive performance in industrial anomaly detection.

Effectively filters anomalous features in latent space.

Abstract

Reconstruction networks are prevalently used in unsupervised anomaly and Out-of-Distribution (OOD) detection due to their independence from labeled anomaly data. However, in multi-class datasets, the effectiveness of anomaly detection is often compromised by the models' generalized reconstruction capabilities, which allow anomalies to blend within the expanded boundaries of normality resulting from the added categories, thereby reducing detection accuracy. We introduce the FUTUREG framework, which incorporates two innovative modules: the Feature Purification Module (FPM) and the CFG Decoder. The FPM constrains the normality boundary within the latent space to effectively filter out anomalous features, while the CFG Decoder uses layer-wise encoder representations to guide the reconstruction of filtered features, preserving fine-grained details. Together, these modules enhance the reconstruction error for anomalies, ensuring high-quality reconstructions for normal samples. Our results demonstrate that FUTUREG achieves state-of-the-art performance in multi-class OOD settings and remains competitive in industrial anomaly detection scenarios.