scholarly journals Sample-Guided Adaptive Class Prototype for Visual Domain Adaptation

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7036
Author(s):  
Chao Han ◽  
Xiaoyang Li ◽  
Zhen Yang ◽  
Deyun Zhou ◽  
Yiyang Zhao ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Sensor Systems ◽  
Testing Scheme ◽  
Cross Domain ◽  
Distribution Matching ◽  
Testing Data ◽  
Discrimination Information ◽  
Matching Strategy ◽  
Visual Domain ◽  
Hard Samples

Domain adaptation aims to handle the distribution mismatch of training and testing data, which achieves dramatic progress in multi-sensor systems. Previous methods align the cross-domain distributions by some statistics, such as the means and variances. Despite their appeal, such methods often fail to model the discriminative structures existing within testing samples. In this paper, we present a sample-guided adaptive class prototype method, which consists of the no distribution matching strategy. Specifically, two adaptive measures are proposed. Firstly, the modified nearest class prototype is raised, which allows more diversity within same class, while keeping most of the class wise discrimination information. Secondly, we put forward an easy-to-hard testing scheme by taking into account the different difficulties in recognizing target samples. Easy samples are classified and selected to assist the prediction of hard samples. Extensive experiments verify the effectiveness of the proposed method.

scholarly journals End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 118630-118638 ◽  
Author(s):  
An-An Liu ◽  
Shu Xiang ◽  
Wei-Zhi Nie ◽  
Dan Song
Keyword(s):  
Domain Adaptation ◽  
Data Retrieval ◽  
Cross Domain ◽  
End To End ◽  
Visual Domain

scholarly journals Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation

2021 ◽  
Vol 11 (10) ◽  
pp. 4503
Author(s):  
Lingtong Min ◽  
Deyun Zhou ◽  
Xiaoyang Li ◽  
Qinyi Lv ◽  
Yuanjie Zhi
Keyword(s):  
Domain Adaptation ◽  
Weather Conditions ◽  
Optimization Method ◽  
Superior Performance ◽  
Target Domain ◽  
Graph Learning ◽  
Learning Framework ◽  
Cross Domain ◽  
Sparse Filtering ◽  
Visual Domain

Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.

scholarly journals Cross-Domain 3D Model Retrieval via Visual Domain Adaption

2018 ◽  
Author(s):  
Anan Liu ◽  
Shu Xiang ◽  
Wenhui Li ◽  
Weizhi Nie ◽  
Yuting Su
Keyword(s):  
Large Scale ◽  
3D Model ◽  
Domain Adaptation ◽  
3D Model Retrieval ◽  
Learning To Learn ◽  
Domain Specific ◽  
Cross Domain ◽  
Model Retrieval ◽  
Modeling Software ◽  
Visual Domain

Recent advances in 3D capturing devices and 3D modeling software have led to extensive and diverse 3D datasets, which usually have different distributions. Cross-domain 3D model retrieval is becoming an important but challenging task. However, existing works mainly focus on 3D model retrieval in a closed dataset, which seriously constrain their implementation for real applications. To address this problem, we propose a novel crossdomain 3D model retrieval method by visual domain adaptation. This method can inherit the advantage of deep learning to learn multi-view visual features in the data-driven manner for 3D model representation. Moreover, it can reduce the domain divergence by exploiting both domainshared and domain-specific features of different domains. Consequently, it can augment the discrimination of visual descriptors for cross-domain similarity measure. Extensive experiments on two popular datasets, under three designed cross-domain scenarios, demonstrate the superiority and effectiveness of the proposed method by comparing against the state-of-the-art methods. Especially, the proposed method can significantly outperform the most recent method for cross-domain 3D model retrieval and the champion of Shrec’16 Large-Scale 3D Shape Retrieval from ShapeNet Core55.

Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

2020 ◽  
Vol 7 (4) ◽  
pp. 1047-1057 ◽  
Author(s):  
Qi Kang ◽  
Siya Yao ◽  
MengChu Zhou ◽  
Kai Zhang ◽  
Abdullah Abusorrah
Keyword(s):  
Domain Adaptation ◽  
Distribution Matching ◽  
Visual Domain

scholarly journals Fault Diagnosis of Gearbox with a Transferable Deep Neural Network

2020 ◽  
Author(s):  
Greg Smith ◽  
Masayoshi Shibatani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Operating Condition ◽  
Cross Domain ◽  
Testing Data

In the past years, various intelligent machine learning and deep learning algorithms have been developed and widely applied for gearbox fault detection and diagnosis. However, the real-time application of these intelligent algorithms has been limited, mainly due to the fact that the model developed using data from one machine or one operating condition has serious diagnosis performance degradation when applied to another machine or the same machine with a different operating condition. The reason for poor model generalization is the distribution discrepancy between the training and testing data. This paper proposes to address this issue using a deep learning based cross domain adaptation approach for gearbox fault diagnosis. Labelled data from training dataset and unlabeled data from testing dataset is used to achieve the cross-domain adaptation task. A deep convolutional neural network (CNN) is used as the main architecture. Maximum mean discrepancy is used as a measure to minimize the distribution distance between the labelled training data and unlabeled testing data. The study proposes to reduce the discrepancy between the two domains in multiple layers of the designed CNN to adapt the learned representations from the training data to be applied in the testing data. The proposed approach is evaluated using experimental data from a gearbox under significant speed variation and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other domain adaptation methods demonstrates the superiority of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document