While huge volumes of unlabeled data are generated and made available in many domains,
the demand for automated understanding of visual data is higher than ever before. Most …

A deep transfer learning framework adapting mixed subdomains is proposed for cross-
species plant disease diagnosis. Most existing deep transfer learning studies focus on …

Unsupervised Domain adaptation is an effective method in addressing the domain shift
issue when transferring knowledge from an existing richly labeled domain to a new domain …

In many real-world scenarios, distribution shifts exist in the streaming data across time steps.
Many complex sequential data can be effectively divided into distinct regimes that exhibit …

Abstract Domain adaptation (DA) mitigates the domain shift problem when transferring
knowledge from one annotated domain to another similar but different unlabeled domain …

Domain adaptation has been used to transfer the knowledge from the source domain to the
target domain where training data is insufficient in the target domain; thus, it can overcome …

Unsupervised transfer learning methods usually exploit the labeled source data to learn a
classifier for unlabeled target data with a different but related distribution. However, most of …

Machining feature segmentation is a primary task in machining feature recognition, as it
directly impacts downstream activities such as feature type identification and process …

In this paper, we study the problem of 3D shape upright orientation estimation from the
perspective of reinforcement learning, ie we teach a machine (agent) to orientate 3D shapes …

Unsupervised domain adaptation (UDA) focuses on transferring knowledge from a labeled
source domain to an unlabeled target domain. However, existing domain adaptation …