Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

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 …

Do visual tasks have a relationship, or are they unrelated? For instance, could having
surface normals simplify estimating the depth of an image? Intuition answers these …

While domain adaptation has been actively researched, most algorithms focus on the single-
source-single-target adaptation setting. In this paper we propose new generalization bounds …

Abstract While Unsupervised Domain Adaptation (UDA) algorithms, ie, there are only
labeled data from source domains, have been actively studied in recent years, most …

In this work, we present a domain flow generation (DLOW) model to bridge two different
domains by generating a continuous sequence of intermediate domains flowing from one …

In computer vision, one is often confronted with problems of domain shifts, which occur when
one applies a classifier trained on a source dataset to target data sharing similar …

Most existing unsupervised domain adaptation (UDA) methods are based upon the
assumption that source labeled data come from an identical underlying distribution …

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 …

In modern industrial equipment maintenance, transfer learning is a promising tool that has
been widely utilized to solve the problem of the insufficient generalization ability of …