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 …

Abstract Domain Adaptation aims to transfer the knowledge learned from a labeled source
domain to an unlabeled target domain whose data distributions are different. However, the …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

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 …

Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies were …

Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-
labeled source domain to a different unlabeled target domain. Most existing UDA methods …

Abstract Domain adaptive object detection is challenging due to distinctive data distribution
between source domain and target domain. In this paper, we propose a unified multi …

Although deep learning models have shown impressive performance on supervised
learning tasks, they often struggle to generalize well when the training (source) and test …

Domain adaptation aims to leverage a label-rich domain (the source domain) to help model
learning in a label-scarce domain (the target domain). Most domain adaptation methods …