Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …

Given a set of pre-trained models, how can we quickly and accurately find the most useful
pre-trained model for a downstream task? Transferability measurement is to quantify how …

How can we accurately recommend actions for users to control their devices at home?
Action recommendation for smart home has attracted increasing attention due to its potential …

Person re-identification is the task of identifying pedestrians across different cameras.
Domain adaptation person re-identification involves transferring knowledge from labeled …

Given multiple source datasets with labels, how can we train a target model with no labeled
data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source …

How can we transfer the knowledge from a source domain to a target domain when each
side cannot observe the data in the other side? Recent transfer learning methods show …