Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple
labeled source domains to an unlabeled target domain. Although current methods achieve …

Learning from multiple modalities has recently attracted increasing attention in many tasks.
However, deep learning-based multi-modal learning cannot guarantee good generalization …

Multistream classification poses significant challenges due to the necessity for rapid
adaptation in dynamic streaming processes with concept drift. Despite the growing research …

Multisource domain adaptation (MDA) aims to transfer knowledge from multiple labeled
source domains to an unlabeled target domain. However, the severe intradomain and …

Facial Expression Recognition (FER) holds significant importance in human-computer
interactions. Existing cross-domain FER methods often transfer knowledge solely from a …

Multi-source domain adaptation (MSDA) learns to predict the labels in target domain data,
under the setting that data from multiple source domains are labelled and data from the …

Multistream classification aims to predict the target stream by transferring knowledge from
labeled source streams amid nonstationary processes with concept drifts. While existing …

Due to the lack of labeled data in many real-world applications, unsupervised domain
adaptation has attracted a great deal of attention in the machine learning community through …

In many practical applications, it is often difficult and expensive to obtain large-scale labeled
data to train state-of-the-art deep neural networks. Therefore, transferring the learned …

In domain adaptation, challenges such as data privacy constraints can impede access to
source data, catalyzing the development of source-free domain adaptation (SFDA) methods …