The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.

Learning the probability distribution of high-dimensional data is a challenging problem.

Measuring and making DNA is central to modern biology and biomedicine.

Motivated by this success, we introduce CADet (Contrastive Anomaly Detection), a novel method for OOD detection of single samples. CADet draws inspiration from MMD, but leverages the similarity between contrastive transformations of a same sample.

Several attempts have been made to explore local significant differences in the past years.