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 …

Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …

Abstract Domain adaptation aims to transfer knowledge in the presence of the domain gap.
Existing domain adaptation methods rely on rich prior knowledge about the relationship …

Object detection typically assumes that training and test data are drawn from an identical
distribution, which, however, does not always hold in practice. Such a distribution mismatch …

In recent years, deep neural networks have emerged as a dominant machine learning tool
for a wide variety of application domains. However, training a deep neural network requires …

Adversarial learning has been embedded into deep networks to learn disentangled and
transferable representations for domain adaptation. Existing adversarial domain adaptation …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

Recent advances in deep domain adaptation reveal that adversarial learning can be
embedded into deep networks to learn transferable features that reduce distribution …

Domain adaptation studies learning algorithms that generalize across source domains and
target domains that exhibit different distributions. Recent studies reveal that deep neural …