Unsupervised domain adaptation (UDA) is a representative problem in transfer learning,
which aims to improve the classification performance on an unlabeled target domain by …

Abstract Domain adaptation has been widely explored by transferring the knowledge from a
label-rich source domain to a related but unlabeled target domain. Most existing domain …

As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA)
has attracted widespread attention in recent years. Previous UDA methods assume the …

As a fundamental manner for learning and cognition, transfer learning has attracted
widespread attention in recent years. Typical transfer learning tasks include unsupervised …

Domain adaptation addresses the learning problem where the training data are sampled
from a source joint distribution (source domain), while the test data are sampled from a …

The lower the distortion of an estimator, the more the distribution of its outputs generally
deviates from the distribution of the signals it attempts to estimate. This phenomenon, known …

As a vital problem in pattern analysis and machine intelligence, Unsupervised Domain
Adaptation (UDA) attempts to transfer an effective feature learner from a labeled source …

Meta-learning, or learning to learn, is a machine learning approach that utilizes prior
learning experiences to expedite the learning process on unseen tasks. As a data-driven …

In the traditional cloud–edge collaborative Internet of Things (IoT), the high-communication
cost and slow convergence of the models often result in high-delay and energy …

Deep learning-based clustering methods, especially those incorporating deep generative
models, have recently shown noticeable improvement on many multimedia benchmark …