Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature Perturbation

One-class anomaly detection aims to detect objects that do not belong to a predefined normal class. In practice, training data lack those anomalous samples; hence, state-of-the-art methods are trained to discriminate between normal and synthetically-generated pseudo-anomalous data. Most methods use data augmentation techniques on normal images to simulate anomalies. However, the best-performing ones implicitly leverage a geometric bias present in the benchmarking datasets. This limits their usability in more general conditions. Others are relying on basic noising schemes that may be suboptimal in capturing the underlying structure of normal data. In addition, most still favour the image domain to generate pseudo-anomalies, training models end-to-end from only the normal class and overlooking richer representations of the information. To overcome these limitations, we consider frozen, yet rich feature spaces given by pretrained models, and create pseudo-anomalous features with a novel adaptive linear feature perturbation technique. It adapts the noise distribution to each sample, applies decaying linear perturbations to feature vectors, and further guides the classification process using a contrastive learning objective. Experimental evaluation conducted on both standard and geometric bias-free datasets demonstrates the superiority of our approach with respect to comparable baselines. The codebase is accessible via our public repository.