Self-Supervised Pre-training on the Target Domain for Cross-Domain Person Re-identification

2021 ◽  
Author(s):  
Junyin Zhang ◽  
Yongxin Ge ◽  
Xinqian Gu ◽  
Boyu Hua ◽  
Tao Xiang
Keyword(s):  
Target Domain ◽  
Cross Domain

scholarly journals Optimal Transport with Dimensionality Reduction for Domain Adaptation

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1994
Author(s):  
Ping Li ◽  
Zhiwei Ni ◽  
Xuhui Zhu ◽  
Juan Song ◽  
Wenying Wu
Keyword(s):  
Dimensionality Reduction ◽  
Optimal Transport ◽  
Domain Adaptation ◽  
Wasserstein Distance ◽  
Local Information ◽  
Target Domain ◽  
Source Domain ◽  
Second Stage ◽  
Cross Domain ◽  
Feature Based

Domain adaptation manages to learn a robust classifier for target domain, using the source domain, but they often follow different distributions. To bridge distribution shift between the two domains, most of previous works aim to align their feature distributions through feature transformation, of which optimal transport for domain adaptation has attract researchers’ interest, as it can exploit the local information of the two domains in the process of mapping the source instances to the target ones by minimizing Wasserstein distance between their feature distributions. However, it may weaken the feature discriminability of source domain, thus degrade domain adaptation performance. To address this problem, this paper proposes a two-stage feature-based adaptation approach, referred to as optimal transport with dimensionality reduction (OTDR). In the first stage, we apply the dimensionality reduction with intradomain variant maximization but source intraclass compactness minimization, to separate data samples as much as possible and enhance the feature discriminability of the source domain. In the second stage, we leverage optimal transport-based technique to preserve the local information of the two domains. Notably, the desirable properties in the first stage can mitigate the degradation of feature discriminability of the source domain in the second stage. Extensive experiments on several cross-domain image datasets validate that OTDR is superior to its competitors in classification accuracy.

scholarly journals Deep Open-Set Domain Adaptation for Cross-Scene Classification based on Adversarial Learning and Pareto Ranking

2020 ◽  
Vol 12 (11) ◽  
pp. 1716
Author(s):  
Reham Adayel ◽  
Yakoub Bazi ◽  
Haikel Alhichri ◽  
Naif Alajlan
Keyword(s):  
High Resolution ◽  
Domain Adaptation ◽  
Target Domain ◽  
Adversarial Learning ◽  
Pareto Ranking ◽  
Cross Domain ◽  
Open Set ◽  
Ranking Scheme ◽  
High Resolution Images ◽  
Alignment Process

Most of the existing domain adaptation (DA) methods proposed in the context of remote sensing imagery assume the presence of the same land-cover classes in the source and target domains. Yet, this assumption is not always realistic in practice as the target domain may contain additional classes unknown to the source leading to the so-called open set DA. Under this challenging setting, the problem turns to reducing the distribution discrepancy between the shared classes in both domains besides the detection of the unknown class samples in the target domain. To deal with the openset problem, we propose an approach based on adversarial learning and pareto-based ranking. In particular, the method leverages the distribution discrepancy between the source and target domains using min-max entropy optimization. During the alignment process, it identifies candidate samples of the unknown class from the target domain through a pareto-based ranking scheme that uses ambiguity criteria based on entropy and the distance to source class prototype. Promising results using two cross-domain datasets that consist of very high resolution and extremely high resolution images, show the effectiveness of the proposed method.

Cross-domain gearbox diagnostics under variable working conditions with deep convolutional transfer learning

2020 ◽  
pp. 107754632093379
Author(s):  
Moslem Azamfar ◽  
Jaskaran Singh ◽  
Xiang Li ◽  
Jay Lee
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
High Performance ◽  
Training Sample ◽  
Training Dataset ◽  
Target Domain ◽  
Training Sample Size ◽  
Vibration Data ◽  
Cross Domain ◽  
Testing Dataset

This study proposes a novel 1D deep convolutional transfer learning method that is able to learn the high-dimensional domain-invariant feature from the labeled training dataset and perform diagnosis tasks on the unlabeled testing dataset subjected to a domain shift. To obtain the domain-invariant features, the cross-entropy loss in the source domain classifier and the maximum mean discrepancies between the source and target domain data are minimized simultaneously. To evaluate the performance of the proposed method, an experimental study is conducted on a gearbox under significant speed variation. Because of inherent limitations of the vibration data, in this research, the effectiveness of torque measurement signals has been explored for gearbox fault diagnosis. Comprehensive studies on network parameters and the training sample size are performed to illustrate the robustness and effectiveness of the proposed method. A comparison study is performed on similar techniques to illustrate the superiority and high performance of the proposed diagnosis method. The achieved results illustrate the effectiveness of torque signal in multiclass cross-domain fault diagnosis of gearboxes.

Inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding for domain adaptation

2020 ◽  
Vol 39 (6) ◽  
pp. 8149-8159
Author(s):  
Ping Li ◽  
Zhiwei Ni ◽  
Xuhui Zhu ◽  
Juan Song
Keyword(s):  
Grassmann Manifold ◽  
Domain Adaptation ◽  
Risk Minimization ◽  
Target Domain ◽  
Class Distribution ◽  
Cross Domain ◽  
Manifold Embedding ◽  
Source Data ◽  
Classifier Adaptation ◽  
Structural Risk

Domain adaptation (DA) aims to train a robust predictor by transferring rich knowledge from a well-labeled source domain to annotate a newly coming target domain; however, the two domains are usually drawn from very different distributions. Most current methods either learn the common features by matching inter-domain feature distributions and training the classifier separately or align inter-domain label distributions to directly obtain an adaptive classifier based on the original features despite feature distortion. Moreover, intra-domain information may be greatly degraded during the DA process; i.e., the source data samples from different classes might grow closer. To this end, this paper proposes a novel DA approach, referred to as inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding (IDAME). Specifically, IDAME commits to adapting the classifier on the Grassmann manifold by using structural risk minimization, where inter-domain feature distributions are aligned to mitigate feature distortion, and the target pseudo labels are exploited using the distances on the Grassmann manifold. During the classifier adaptation process, we simultaneously consider the inter-class distribution alienation, the inter-domain distribution alignment, and the manifold consistency. Extensive experiments validate that IDAME can outperform several comparative state-of-the-art methods on real-world cross-domain image datasets.

scholarly journals Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 473
Author(s):  
Yongpeng Wang ◽  
Hong Yu ◽  
Guoyin Wang ◽  
Yongfang Xie
Keyword(s):  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Mapping Function ◽  
Linear Mapping ◽  
Feature Mapping ◽  
Target Domain ◽  
User Reviews ◽  
Cross Domain ◽  
Semantic Orientation ◽  
Label Information

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

scholarly journals Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

2019 ◽  
Vol 33 ◽  
pp. 94-101 ◽  
Author(s):  
Wenjing Fu ◽  
Zhaohui Peng ◽  
Senzhang Wang ◽  
Yang Xu ◽  
Jin Li
Keyword(s):  
Semantic Information ◽  
Side Information ◽  
Recommendation Systems ◽  
Cold Start ◽  
Superior Performance ◽  
Fusion Model ◽  
Target Domain ◽  
Latent Factors ◽  
Data Sparsity ◽  
Cross Domain

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

scholarly journals A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xu Yu ◽  
Jun-yu Lin ◽  
Feng Jiang ◽  
Jun-wei Du ◽  
Ji-zhong Han
Keyword(s):  
Linear Regression ◽  
Collaborative Filtering ◽  
Feature Construction ◽  
Target Domain ◽  
Regression Problem ◽  
Filtering Algorithm ◽  
Weighted Linear Regression ◽  
Cross Domain ◽  
Collaborative Filtering Algorithm ◽  
Sparsity Problem

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

scholarly journals Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)

2018 ◽  
Author(s):  
Alejandro Moreo Fernández ◽  
Andrea Esuli ◽  
Fabrizio Sebastiani
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Training Data ◽  
Target Domain ◽  
Source Domain ◽  
Machine Learning Methods ◽  
Cross Domain ◽  
Current State ◽  
Cross Lingual

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a “target” domain when the only available training data belongs to a different “source” domain. In this extended abstract, we briefly describe our new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification.

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.

scholarly journals Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2510
Author(s):  
Nam D. Vo ◽  
Minsung Hong ◽  
Jason J. Jung
Keyword(s):  
Gradient Descent ◽  
Recommendation System ◽  
Computation Time ◽  
Descent Method ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Gradient Descent Method ◽  
Target Domain ◽  
Cross Domain ◽  
Gradient Descent Algorithm

The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity.

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