Since deep learning models have been implemented in many commercial applications, it is
important to detect out-of-distribution (OOD) inputs correctly to maintain the performance of …

The healthcare system is in crisis due to challenges including escalating costs, the
inconsistent provision of care, an aging population, and high burden of chronic disease …

Deep active learning aims to reduce the annotation cost for the training of deep models,
which is notoriously data-hungry. Until recently, deep active learning methods were …

Current local density-based anomaly detection methods are limited in that the local density
estimation and the neighborhood density estimation are not accurate enough for complex …

Novelty detection is especially important for monitoring safety-critical systems in which novel
conditions rarely occur and knowledge about novelty in that system is often limited or …

In decision making systems involving multiple classifiers there is the need to assess
classifier (in) congruence, that is to gauge the degree of agreement between their outputs. A …

Interestingness is said to be the power of attracting or holding one's attention (because
something is unusual or exciting, etc.). We, as humans, have the great capacity to direct our …

We address the problem of anomaly detection in machine perception. The concept of
domain anomaly is introduced as distinct from the conventional notion of anomaly used in …

As the sheer volume of available benchmark datasets increases, the problem of joint
learning of classifiers and knowledge-transfer between classifiers, becomes more and more …

Dimensionality reduction is one of the most used transformations of data and plays a critical
role in maintaining meaningful properties while transforming data from high-to low …