Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a
labeled source domain to an unlabeled target domain under the distribution shift. Thanks to …

Supervised learning with large scale labelled datasets and deep layered models has
caused a paradigm shift in diverse areas in learning and recognition. However, this …

Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target
domain by leveraging knowledge from a labeled source domain with a different but related …

Unsupervised domain adaptation (UDA) aims to generalize the supervised model trained on
a source domain to an unlabeled target domain. Previous works mainly rely on the marginal …

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 …

Unsupervised domain adaptation (UDA) deals with the problem of transferring knowledge
from a labeled source domain to an unlabeled target domain when the two domains have …

In the presence of large sets of labeled data, Deep Learning (DL) has accomplished
extraordinary triumphs in the avenue of computer vision, particularly in object classification …

Deep neural networks, trained with large amount of labeled data, can fail to generalize well
when tested with examples from a target domain whose distribution differs from the training …