Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

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 …

Abstract Domain generalization (DG) is essentially an out-of-distribution problem, aiming to
generalize the knowledge learned from multiple source domains to an unseen target …

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 …

This paper focuses on domain generalization (DG), the task of learning from multiple source
domains a model that generalizes well to unseen domains. A main challenge for DG is that …

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …

Though remarkable progress has been achieved in various vision tasks, deep neural
networks still suffer obvious performance degradation when tested in out-of-distribution …

In this paper, we present an Adaptive Ensemble Learning framework that aims to boost the
performance of deep neural networks by intelligently fusing features through ensemble …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …