In traditional machine learning algorithms, the classification models are learned on the
training data (source domain) to reuse for labelling the test data (target domain) where the …

Abstract Domain shift is defined as the mismatch between the marginal probability
distributions of a source (training set) and a target domain (test set). A successful research …

As a branch of transfer learning, domain adaptation leverages useful knowledge from a
source domain to a target domain for solving target tasks. Most of the existing domain …

Unsupervised domain adaptation is intended to construct a reliable model for the unlabeled
target samples using the well-labeled but differently distributed source samples. To tackle …

Unsupervised domain adaptation (UDA) aims to explore the knowledge of labeled source
domain to help training the model of unlabeled target domain. By now, while most existing …

Domain adaptation (DA) deals with the common problem of distribution mismatch between
the training and the target data. A model tested on data that comes from a different source …

Unsupervised Domain Adaptation (UDA) aims to leverage the knowledge from the labeled
source domain to help the task of target domain with the unlabeled data. It is a key step for …

In traditional machine learning, there is often a discrepancy in data distribution between the
source and target domains. Domain adaptation (DA) was proposed to learn the robust …

Supervised learning algorithms like KNN assume that the training and the testing data come
from the same source, and hence, have the same distribution. However, in real life, such …

We address the source-free domain adaptation (SFDA) problem, where only the source
model is available during adaptation to the target domain. We consider two settings: the …