Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the …
In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is …
Abstract Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully- labeled source domain to an unlabeled target domain. In some applications, however, it is …
J Na, H Jung, HJ Chang… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were …
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a …
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively …
Training models that generalize to new domains at test time is a problem of fundamental importance in machine learning. In this work, we encode this notion of domain …
Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these …
Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared …