Conventional machine learning relies on two presumptions:(1) the training and testing
datasets follow the same independent distribution, and (2) an adequate quantity of samples …

Unsupervised domain adaptation (UDA) tries to overcome the need of a large labeled
dataset by transferring knowledge from a source dataset, with lots of labeled data, to a target …

Experiencing domain shifts during test-time is nearly inevitable in practice and likely results
in a severe performance degradation. To overcome this issue, test-time adaptation …

Standard domain adaptation methods do not work well when a large gap exists between the
source and target domains. Gradual domain adaptation is one of the approaches used to …

In supervised learning problems, it is common to have a lot of unlabeled data, but little
labeled data. It is then desirable to leverage the unlabeled data to improve the learning …

Unsupervised domain adaptation (UDA) is a critical challenge in the field of point cloud
analysis. Previous works tackle the problem either by feature extractor adaptation to enable …

In domain adaptation, when there is a large distance between the source and target
domains, the prediction performance will degrade. Gradual domain adaptation is one of the …

The aim of this paper is to address the challenge of gradual domain adaptation within a
class of manifold-constrained data distributions. In particular, we consider a sequence of …

In domain adaptation, when there is a large distance between the source and target
domains, the prediction performance will degrade. Gradual domain adaptation is one of the …

This paper proposes an iterative intermediate domain generation method using low-and
high-pass filters. Domain shift is one of the prime reasons for the poor generalization of …