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 the past years, data-driven approaches such as deep learning have been widely applied
on machinery signal processing to develop intelligent fault diagnosis systems. In real-world …

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 …

Classifiers trained on given databases perform poorly when tested on data acquired in
different settings. This is explained in domain adaptation through a shift among distributions …

Intelligent data-driven fault diagnosis methods for rolling element bearings have been
widely developed in the recent years. In real industries, the collected machinery signals are …

Abstract Current Domain Adaptation (DA) methods based on deep architectures assume
that the source samples arise from a single distribution. However, in practice most datasets …

While fine-grained object recognition is an important problem in computer vision, current
models are unlikely to accurately classify objects in the wild. These fully supervised models …

Abstract Domain adaptation is an important tool to transfer knowledge about a task (eg
classification) learned in a source domain to a second, or target domain. Current …

Unsupervised domain adaptation aims to learn a classification model for the target domain
without any labeled samples by transferring the knowledge from the source domain with …

Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in
real-world scenarios, which relaxes the assumption in conventional Unsupervised Domain …