Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into
transfer learning (TL), has achieved excellent success in computer vision, text classification …

Abstract Domain adversarial training has been ubiquitous for achieving invariant
representations and is used widely for various domain adaptation tasks. In recent times …

A fundamental challenge for machine learning models is how to generalize learned models
for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features …

Transfer learning refers to the transfer of knowledge or information from a relevant source
domain to a target domain. However, most existing transfer learning theories and algorithms …

Modern deep learning systems require huge data sets to achieve impressive performance,
but there is little guidance on how much or what kind of data to collect. Over-collecting data …

Source free domain adaptation (SFDA) transfers a single-source model to the unlabeled
target domain without accessing the source data. With the intelligence development of …

Active learning is the process of training a model with limited labeled data by selecting a
core subset of an unlabeled data pool to label. The large scale of data sets used in deep …

Abstract Multi-Source Domain Adaptation (MSDA) aims at training a classification model that
achieves small target error, by leveraging labeled data from multiple source domains and …

In this paper, we study the problem of domain adaptation regression, which learns a
regressor for a target domain by leveraging the knowledge from a relevant source domain …

Domain adaptation is an important problem and often needed for real-world applications. In
this problem, instead of iid training and testing datapoints, we assume that the source …