Structure Preservation Adversarial Network for Visual Domain Adaptation

2021 ◽  
Author(s):  
Min Meng ◽  
Qiguang Chen ◽  
Jigang Wu
Keyword(s):  
Domain Adaptation ◽  
Structure Preservation ◽  
Adversarial Network ◽  
Visual Domain

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

scholarly journals Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 524
Author(s):  
Kyoung Jun Noh ◽  
Jiho Choi ◽  
Jin Seong Hong ◽  
Kang Ryoung Park
Keyword(s):  
Domain Adaptation ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Heterogeneous Databases ◽  
Second Best ◽  
Finger Vein ◽  
Adversarial Network ◽  
Image Characteristics ◽  
Vein Recognition ◽  
Finger Vein Recognition

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

Multi-source based approach for Visual Domain Adaptation

2021 ◽  
Author(s):  
Mrinalini Tiwari ◽  
Rakesh Kumar Sanodiya ◽  
Jimson Mathew ◽  
Sriparna Saha
Keyword(s):  
Domain Adaptation ◽  
Visual Domain

scholarly journals Visual Domain Adaptation with Manifold Embedded Distribution Alignment

2018 ◽  
Author(s):  
Jindong Wang ◽  
Wenjie Feng ◽  
Yiqiang Chen ◽  
Han Yu ◽  
Meiyu Huang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Visual Domain

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

Sparse and Low-Rank Models for Visual Domain Adaptation

2016 ◽  
pp. 113-133
Author(s):  
Rama Chellappa ◽  
Vishal M. Patel
Keyword(s):  
Domain Adaptation ◽  
Low Rank ◽  
Low Rank Models ◽  
Visual Domain

scholarly journals Spectral Normalization for Domain Adaptation

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 68
Author(s):  
Liquan Zhao ◽  
Yan Liu
Keyword(s):  
Transfer Learning ◽  
Predictive Accuracy ◽  
Domain Adaptation ◽  
Learning Performance ◽  
Constraint Condition ◽  
Adversarial Network ◽  
Network Methods ◽  
The Stability ◽  
Spectral Normalization ◽  
Adaptation Method

The transfer learning method is used to extend our existing model to more difficult scenarios, thereby accelerating the training process and improving learning performance. The conditional adversarial domain adaptation method proposed in 2018 is a particular type of transfer learning. It uses the domain discriminator to identify which images the extracted features belong to. The features are obtained from the feature extraction network. The stability of the domain discriminator directly affects the classification accuracy. Here, we propose a new algorithm to improve the predictive accuracy. First, we introduce the Lipschitz constraint condition into domain adaptation. If the constraint condition can be satisfied, the method will be stable. Second, we analyze how to make the gradient satisfy the condition, thereby deducing the modified gradient via the spectrum regularization method. The modified gradient is then used to update the parameter matrix. The proposed method is compared to the ResNet-50, deep adaptation network, domain adversarial neural network, joint adaptation network, and conditional domain adversarial network methods using the datasets that are found in Office-31, ImageCLEF-DA, and Office-Home. The simulations demonstrate that the proposed method has a better performance than other methods with respect to accuracy.

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