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 Discriminative Sparse Filtering for Multi-Source Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5868 ◽  
Author(s):  
Chao Han ◽  
Deyun Zhou ◽  
Zhen Yang ◽  
Yu Xie ◽  
Kai Zhang
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Source Image ◽  
Unified Framework ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Source Domain ◽  
Cross Domain ◽  
Sparse Filtering ◽  
Novel Approach

Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.

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.

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 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.

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 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 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.

Sign in / Sign up

Export Citation Format

Share Document