Adversarial Domain Alignment Feature Similarity Enhancement Learning for Unsupervised Domain Adaptation

2019 ◽  
pp. 259-271
Author(s):  
Jun Zhou ◽  
Fei Wu ◽  
Ying Sun ◽  
Songsong Wu ◽  
Min Yang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Feature Similarity ◽  
Domain Alignment

scholarly journals Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation

2017 ◽  
pp. 357-369 ◽  
Author(s):  
Fabio Maria Carlucci ◽  
Lorenzo Porzi ◽  
Barbara Caputo ◽  
Elisa Ricci ◽  
Samuel Rota Bulò
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Alignment Layers ◽  
Domain Alignment

MultiDIAL: Domain Alignment Layers for (Multisource) Unsupervised Domain Adaptation

2020 ◽  
pp. 1-1
Author(s):  
Fabio Maria Carlucci ◽  
Lorenzo Porzi ◽  
Barbara Caputo ◽  
Elisa Ricci ◽  
Samuel Rota Bulo
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Alignment Layers ◽  
Domain Alignment

scholarly journals MS-DIAL: Multi-Source Domain Alignment Layers for Unsupervised Domain Adaptation

2020 ◽  
Author(s):  
Lucas Fernando Alvarenga e Silva ◽  
Jurandy Almeida
Keyword(s):  
Deep Neural Networks ◽  
Domain Adaptation ◽  
Research Area ◽  
Multiple Sources ◽  
Source Domain ◽  
Unsupervised Domain Adaptation ◽  
Public Datasets ◽  
Alignment Layers ◽  
Domain Alignment ◽  
Different Sources

In general, deep neural networks trained on a given labeled dataset are expected to produce equivalent results when tested on a new unlabeled dataset. However, data are generally collected by different devices or under varying conditions and thus they often are not part of a same domain, yielding poor results. This is due to the domain shift between data distributions and has been the goal of a research area known as unsupervised domain adaptation. Many prior works have been designed to transfer knowledge between two domains: one source to one target. Since data may be taken from different sources and with different distributions, multi-source domain adaptation has received increasing attention. This paper presents the Multi-Source DomaIn Alignment Layers (MS-DIAL), which reduce the domain shift between multiple sources and a given target by embedding domain alignment layers in any given network. Except for the embedded layers, all the other network parameters are shared among all domains, saving processing time and memory usage. Experiments were performed on digit and object recognition tasks with five public datasets widely used to evaluate domain adaptation methods. Results show that the proposed method is promising and outperforms state-of-the-art approaches.

scholarly journals Bi-Directional Generation for Unsupervised Domain Adaptation

2020 ◽  
Vol 34 (04) ◽  
pp. 6615-6622 ◽  
Author(s):  
Guanglei Yang ◽  
Haifeng Xia ◽  
Mingli Ding ◽  
Zhengming Ding
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Target Domain ◽  
Unsupervised Domain Adaptation ◽  
Cross Domain ◽  
Proposed Model ◽  
Latent Space ◽  
Domain Alignment ◽  
Domain Information ◽  
Adaptation Model

Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classifiers and the cross-domain alignment constraints. We also design two classifiers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks.

Importance-weighted conditional adversarial network for unsupervised domain adaptation

2020 ◽  
Vol 155 ◽  
pp. 113404 ◽  
Author(s):  
Peng Liu ◽  
Ting Xiao ◽  
Cangning Fan ◽  
Wei Zhao ◽  
Xianglong Tang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Network
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