We consider the problem of anomaly detection in images, and present a new detectiontechnique. Givenasampleofimages,allknowntobelongtoa"normal" class (e.g., dogs), we show how to train a deep neural model that can detect out-of-distribution images (i.e., non-dog objects).

Neural Information Processing Systems http://nips.cc/

Afterwards, based onthe enriched representation, we propose one novel graphpointnet module, relyingonthegraphattention blocktodynamically compose and update each point representation within the local point cloud structure.

Figure 1: Prediction risk of ridge regression on trained CK features (erf) after one feature learning step.

Empirical data can often be considered as samples from a set of probability distributions.

Wedemonstrate theproposed methodology onahypertension dataset,showingthatourprescribed treatment leadstoalarger reduction in the systolic blood pressure compared to a series of alternatives.