Reconstruction-Based Anomaly Detection in Positive-Unlabeled Reverse Distillation

Reverse distillation is one of the strongest paradigms for visual anomaly detection, training a student decoder to reconstruct a frozen teacher encoder's representations so that unseen anomalies yield disproportionately large reconstruction errors. However, this paradigm implicitly assumes a training pool of only normal samples. In practice, such pool is often contaminated, modeled as a mixture of normal and anomalous distributions at an unknown rate, and training directly on this mixture provides no pressure to separate the two populations. Positive-unlabeled learning (PUL) offers a natural way to address this, incorporating a small labeled anomalous set to guide this separation. However, existing PUL approaches largely rely on classification-level objectives that offer little sense of where an anomaly is located. Instead, we build directly on the spatial structure of reconstruction error in feature space, which already carries information about where an anomaly might be located, without requiring pixel-level annotation. We propose an objective that thresholds each image's own feature-space reconstruction error into anomalous and normal regions and applies separation pressure between them. We apply this pressure asymmetrically. For labeled anomalies, whose status is certain, we push the two regions apart in both directions. For unlabeled samples, we only push the flagged region's error down. Evaluated on Real-IAD, an industrial anomaly benchmark, our method consistently improves over the uncorrected baseline and recovers much of the performance gap to an oracle trained without contamination.