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 Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model
trained on a source domain to detect instances from a new target domain for which …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

Over the past decade, the dominance of deep learning has prevailed across various
domains of artificial intelligence, including natural language processing, computer vision …

We consider the problem of active domain adaptation (ADA) to unlabeled target data, of
which subset is actively selected and labeled given a budget constraint. Inspired by recent …

The data-hungry problem, characterized by insufficiency and low-quality of data, poses
obstacles for deep learning models. Transfer learning has been a feasible way to transfer …

In multimodal machine learning, proper handling of cross-modal information is essential for
obtaining an ideal joint embedding. Despite the progress made by recent fusion strategies …

A well-known obstacle in the successful implementation of deep learning-based systems to
real-world problems is the performance degradation that occurs when applying a network …

We address the problem of generalized category discovery (GCD) that aims to partition a
partially labeled collection of images; only a small part of the collection is labeled and the …