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 …

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 …

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 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 …

The performance of a classifier trained on data coming from a specific domain typically
degrades when applied to a related but different one. While annotating many samples from …

Domain adaptation nowadays attracts increasing interests in pattern recognition and
computer vision field, since it is an appealing technique in fighting off weakly labeled or …

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 …

Deep domain adaptation methods have achieved appealing performance by learning
transferable representations from a well-labeled source domain to a different but related …

Deep domain adaptation (DDA) approaches have recently been shown to perform better
than their shallow rivals with better modeling capacity on complex domains (eg, image …

Unsupervised domain adaptation, as a prevalent transfer learning setting, spans many real-
world applications. With the increasing representational power and applicability of neural …