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 …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Transfer learning, which allows a source task to affect the inductive bias of the target task, is
widely used in computer vision. The typical way of conducting transfer learning with deep …

This work addresses the problem of domain adaptation and generalization in a unified
fashion. The main idea is to exploit the siamese architecture with the Contrastive Loss to …

This work provides a framework for addressing the problem of supervised domain
adaptation with deep models. The main idea is to exploit adversarial learning to learn an …

The performance of a classifier trained on data coming from a specific domain typically
degrades when applied to a related but different one. While annotating many samples from …

Regular machine learning and data mining techniques study the training data for future
inferences under a major assumption that the future data are within the same feature space …

In pattern recognition and computer vision, one is often faced with scenarios where the
training data used to learn a model have different distribution from the data on which the …

To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer
knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset …

In real-world applications of visual recognition, many factors-such as pose, illumination, or
image quality-can cause a significant mismatch between the source domain on which …