Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Real-world data often exhibit imbalanced distributions, where certain target values have
significantly fewer observations. Existing techniques for dealing with imbalanced data focus …

Many recent machine learning tasks focus to develop models that can generalize to unseen
distributions. Domain generalization (DG) has become one of the key topics in various fields …

To minimize the effects of age variation in face recognition, previous work either extracts
identity-related discriminative features by minimizing the correlation between identity-and …

Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …

A fundamental challenge for machine learning models is how to generalize learned models
for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features …

Temporal domain generalization is a promising yet extremely challenging area where the
goal is to learn models under temporally changing data distributions and generalize to …