  Universal anomaly detection still remains a challenging problem in machine
learning and medical image analysis. It is possible to learn an expected
distribution from a single class of normative samples, e.g., through epistemic
uncertainty estimates, auto-encoding models, or from synthetic anomalies in a
self-supervised way. The performance of self-supervised anomaly detection
approaches is still inferior compared to methods that use examples from known
unknown classes to shape the decision boundary. However, outlier exposure
methods often do not identify unknown unknowns. Here we discuss an improved
self-supervised single-class training strategy that supports the approximation
of probabilistic inference with loosen feature locality constraints. We show
that up-scaling of gradients with histogram-equalised images is beneficial for
recently proposed self-supervision tasks. Our method is integrated into several
out-of-distribution (OOD) detection models and we show evidence that our method
outperforms the state-of-the-art on various benchmark datasets.
