Anomaly detection for time-series data is crucial in the management of systems for
streaming applications, computational services, and cloud platforms. The majority of current …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

Universal domain adaptive object detection (UniDAOD) is a more challenging and realistic
problem than traditional domain adaptive object detection (DAOD), aiming to transfer the …

Unsupervised domain adaptation is an important branch of transfer learning, which mainly
solves the generalization problem of models from fully labeled datasets to unlabeled …

Recently, unsupervised domain adaptation methods have been increasingly applied to
address the domain shift problems in defect detection. However, the effectiveness of most …

Although logit distillation aims to transfer knowledge from a large teacher network to a
student, the underlying mechanisms and reasons for its effectiveness are unclear. This …

Unsupervised domain adaptation (UDA) attempts to learn domain invariant representations
and has achieved significant progress, whereas self-training-based UDA methods have …

For domain adaptation in open-set scenarios, target domain samples may be collected from
unknown object categories, which are not associated with the original source domain. It is …

Semi-supervised learning techniques utilize both labeled and unlabeled images to enhance
classification performance in scenarios where labeled images are limited. However …

Abstract Domain adaptation, the important branch of transfer learning, aims to solve the
problem that the performance of traditional machine learning algorithms drops sharply when …