Incorporating additional knowledge in the learning process can be beneficial for several
computer vision tasks. Whether privileged information originates from a source domain that …

The transfer learning and domain adaptation problems originate from a distribution
mismatch between the source and target data distribution. The causes of such mismatch are …

Abstract Domain-invariant representations are key to addressing the domain shift problem
where the training and test examples follow different distributions. Existing techniques that …

Most successful object classification and detection methods rely on classifiers trained on
large labeled datasets. However, for domains where labels are limited, simply borrowing …

The objective of unsupervised domain adaptation is to leverage features from a labeled
source domain and learn a classifier for an unlabeled target domain, with a similar but …

Abstract Universal Domain Adaptation aims to transfer the knowledge between the datasets
by handling two shifts: domain-shift and category-shift. The main challenge is correctly …

Visual domain adaptation, which learns an accurate classifier for a new domain using
labeled images from an old domain, has shown promising value in computer vision yet still …

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to
transfer the knowledge from source to target under unaligned label space. The main …

This paper introduces a learning scheme to construct a Hilbert space (ie, a vector space
along its inner product) to address both unsupervised and semi-supervised domain …

Transferring knowledge from an existing labeled domain to a new domain often suffers from
domain shift in which performance degrades because of differences between the domains …