Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

The discovery of several machine learning and deep learning techniques has paved the
way to extend the reach of humans in various real-world applications. Classical machine …

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 …

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-
scale testbed for unsupervised domain adaptation across visual domains. Unsupervised …

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

The recent success of deep neural networks relies on massive amounts of labeled data. For
a target task where labeled data is unavailable, domain adaptation can transfer a learner …

The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

We propose a new approach, called self-motivated pyramid curriculum domain adaptation
(PyCDA), to facilitate the adaptation of semantic segmentation neural networks from …

Abstract Domain shift is unavoidable in real-world applications of object detection. For
example, in self-driving cars, the target domain consists of unconstrained road environments …

In order to robustly deploy object detectors across a wide range of scenarios, they should be
adaptable to shifts in the input distribution without the need to constantly annotate new data …