The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Active learning aims to train an accurate prediction model with minimum cost by labeling
most informative instances. In this paper, we survey existing works on active learning from …

To mitigate the detection performance drop caused by domain shift, we aim to develop a
novel few-shot adaptation approach that requires only a few target domain images with …

We propose an active learning approach for transferring representations across domains.
Our approach, active adversarial domain adaptation (AADA), explores a duality between two …

Unsupervised domain adaptation (UDA) enables transferring knowledge from a related
source domain to a fully unlabeled target domain. Despite the significant advances in UDA …

We aim to understand the value of additional labeled or unlabeled target data in transfer
learning, for any given amount of source data; this is motivated by practical questions …

域适应主要应对跨不同数据分布的相似任务决策问题. 作为机器学习领域的一个新兴分支,
域适应受到了众多的研究和关注. 随着近年深度学习的兴起, 深度学习和域适应相结合的深度域 …

Unsupervised domain adaptation (UDA) aims at transferring knowledge from one labeled
source domain to a related but unlabeled target domain. Recently, active domain adaptation …

Visual domain adaptation aims to seek an effective transferable model for unlabeled target
images by benefiting from the well-labeled source images following different distributions …