Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …

Abstract Unsupervised Domain Adaptation solves knowledge transfer along with the
coexistence of well-annotated source domain and unlabeled target instances. However, the …

Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …

In this paper we investigate problematic practices and consequences of large scale vision
datasets (LSVDs). We examine broad issues such as the question of consent and justice as …

In this paper, we investigate a challenging unsupervised domain adaptation setting---
unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data …

Recent advances in domain adaptation show that deep self-training presents a powerful
means for unsupervised domain adaptation. These methods often involve an iterative …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Instance contrast for unsupervised representation learning has achieved great success in
recent years. In this work, we explore the idea of instance contrastive learning in …

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 …

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