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 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 …

Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging
knowledge from a source domain. Existing methods either attempt to align the cross-domain …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

Deep neural networks (DNN) have shown unprecedented success in various computer
vision applications such as image classification and object detection. However, it is still a …

In this paper, we propose a novel unsupervised domain adaptation algorithm based on
deep learning for visual object recognition. Specifically, we design a new model called Deep …

During the last half decade, convolutional neural networks (CNNs) have triumphed over
semantic segmentation, which is a core task of various emerging industrial applications such …

The learning of domain-invariant representations in the context of domain adaptation with
neural networks is considered. We propose a new regularization method that minimizes the …

Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

Deep neural networks (DNN) have shown unprecedented success in various computer
vision applications such as image classification and object detection. However, it is still a …