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 …

The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …

Abstract Domain generalization refers to the problem of training a model from a collection of
different source domains that can directly generalize to the unseen target domains. A …

In a joint vision-language space, a text feature (eg, from" a photo of a dog") could effectively
represent its relevant image features (eg, from dog photos). Also, a recent study has …

Abstract Domain generalization (DG) aims to learn a generalized model to an unseen target
domain using only limited source domains. Previous attempts to DG fail to learn domain …

Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …

The main challenge in domain generalization (DG) is to handle the distribution shift problem
that lies between the training and test data. Recent studies suggest that test-time training …

Abstract Domain generalization (DG) seeks predictors which perform well on unseen test
distributions by leveraging data drawn from multiple related training distributions or …