Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Despite the great success achieved by supervised fully convolutional models in semantic
segmentation, training the models requires a large amount of labor-intensive work to …

Learning an effective representation of 3D point clouds requires a good metric to measure
the discrepancy between two 3D point sets, which is non-trivial due to their irregularity. Most …

Abstract Multi-source Domain Adaptation (MSDA) is more practical but challenging than the
conventional unsupervised domain adaptation due to the involvement of diverse multiple …

Multi-source domain adaptation (DA) is more challenging than conventional DA because the
knowledge is transferred from several source domains to a target domain. To this end, we …

Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory
issue of OT in large-scale applications. Despite their practicality, m-OT suffers from …

It is well-known that deep neural networks (DNNs) are susceptible to adversarial attacks,
exposing a severe fragility of deep learning systems. As the result, adversarial training (AT) …

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to
handle the recommendation problem under cold-start settings. Specifically, we divide the …