  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.
