Canonical Correlation Cross-Domain Alignment for Unsupervised Domain Adaptation

2020 ◽  
pp. 395-406
Author(s):  
Desheng Li ◽  
Wenjing Wang ◽  
Yuwu Lu
Keyword(s):  
Canonical Correlation ◽  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Cross Domain ◽  
Domain Alignment

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.

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

scholarly journals FeatureTransfer: Unsupervised Domain Adaptation for Cross-Domain Deepfake Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Baoying Chen ◽  
Shunquan Tan
Keyword(s):  
Large Scale ◽  
Detection Method ◽  
Domain Adaptation ◽  
Third Party ◽  
Detection Methods ◽  
Target Domain ◽  
Feature Vectors ◽  
Unsupervised Domain Adaptation ◽  
Cross Domain ◽  
Overfitting Problem

Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). These detection methods suffer from overfitting on the source dataset and do not perform well on cross-domain datasets which have different distributions from the source dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, which is a two-stage Deepfake detection method combining with transfer learning. Firstly, The CNN model pretrained on a third-party large-scale Deepfake dataset can be used to extract the more transferable feature vectors of Deepfake videos in the source and target domains. Secondly, these feature vectors are fed into the domain-adversarial neural network based on backpropagation (BP-DANN) for unsupervised domain adaptive training, where the videos in the source domain have real or fake labels, while the videos in the target domain are unlabelled. The experimental results indicate that the proposed method FeatureTransfer can effectively solve the overfitting problem in Deepfake detection and greatly improve the performance of cross-dataset evaluation.

Unsupervised Domain Adaptation with Self-selected Active Learning for Cross-domain OCT Image Segmentation

2021 ◽  
pp. 585-596
Author(s):  
Xiaohui Li ◽  
Sijie Niu ◽  
Xizhan Gao ◽  
Tingting Liu ◽  
Jiwen Dong
Keyword(s):  
Image Segmentation ◽  
Active Learning ◽  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Cross Domain ◽  
Oct Image
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