scholarly journals Information-Theoretic Multi-view Domain Adaptation: A Theoretical and Empirical Study

2014 ◽  
Vol 49 ◽  
pp. 501-525 ◽  
Author(s):  
P. Yang ◽  
W. Gao
Keyword(s):  
Domain Adaptation ◽  
Classification Performance ◽  
Transfer Model ◽  
Multiple Views ◽  
Target Domain ◽  
Adaptation Algorithm ◽  
Information Theoretic ◽  
Single View ◽  
Domain Specific ◽  
Cross Domain

Multi-view learning aims to improve classification performance by leveraging the consistency among different views of data. The incorporation of multiple views was paid little attention in the studies of domain adaptation, where the view consistency based on source data is largely violated in the target domain due to the distribution gap between different domain data. In this paper, we leverage multiple views for cross-domain document classification. The central idea is to strengthen the views' consistency on target data by identifying the associations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) using a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated features across domains, which boosts the consistency between document clusterings that are based on the respective word and link views. Moreover, we demonstrate that IMAM can always find the document clustering with the minimal disagreement rate to the overlap of view-based clusterings. We provide both theoretical and empirical justifications of the proposed method. Our experiments show that IMAM significantly outperforms traditional multi-view algorithm co-training, the co-training-based adaptation algorithm CODA, the single-view transfer model CoCC and the large-margin-based multi-view transfer model MVTL-LM.

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 Domain Conditioned Adaptation Network

2020 ◽  
Vol 34 (07) ◽  
pp. 11386-11393 ◽  
Author(s):  
Shuang Li ◽  
Chi Liu ◽  
Qiuxia Lin ◽  
Binhui Xie ◽  
Zhengming Ding ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Feature Learning ◽  
Feature Representations ◽  
Domain Specific ◽  
Cross Domain ◽  
Learning Tasks ◽  
Domain Learning ◽  
High Level ◽  
High Level Feature ◽  
Target Data

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.

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.

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 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 Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

2020 ◽  
Vol 34 (04) ◽  
pp. 6243-6250 ◽  
Author(s):  
Qian Wang ◽  
Toby Breckon
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Structured Prediction ◽  
Target Domain ◽  
Source Domain ◽  
Domain Specific ◽  
Unsupervised Domain Adaptation ◽  
Deep Feature ◽  
Significant Performance ◽  
Error Accumulation

Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain and the data distributions are different in these two domains. As a result, classifiers trained from labeled samples in the source domain suffer from significant performance drop when directly applied to the samples from the target domain. To address this issue, different approaches have been proposed to learn domain-invariant features or domain-specific classifiers. In either case, the lack of labeled samples in the target domain can be an issue which is usually overcome by pseudo-labeling. Inaccurate pseudo-labeling, however, could result in catastrophic error accumulation during learning. In this paper, we propose a novel selective pseudo-labeling strategy based on structured prediction. The idea of structured prediction is inspired by the fact that samples in the target domain are well clustered within the deep feature space so that unsupervised clustering analysis can be used to facilitate accurate pseudo-labeling. Experimental results on four datasets (i.e. Office-Caltech, Office31, ImageCLEF-DA and Office-Home) validate our approach outperforms contemporary state-of-the-art methods.

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 Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1270
Author(s):  
Chenhui Ma ◽  
Dexuan Sha ◽  
Xiaodong Mu
Keyword(s):  
Remote Sensing ◽  
Domain Adaptation ◽  
Training Data ◽  
Scene Classification ◽  
Target Domain ◽  
Domain Specific ◽  
Invariant Features ◽  
Distribution Matching ◽  
Feature Adaptation ◽  
Public Datasets

Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from source or target domains. However, they suffer from the following two limitations: (1) the classifier is trained on source samples and forms a source-domain-specific boundary, which ignores features from the target domain and (2) semantically meaningful features are merely built from the adversary of a generator and a discriminator, which ignore selecting the domain invariant features. These issues limit the distribution matching performance of source and target domains, since each domain has its distinctive characteristic. To resolve these issues, we propose a framework with error-correcting boundaries and feature adaptation metric. Specifically, we design an error-correcting boundaries mechanism to build target-domain-specific classifier boundaries via multi-classifiers and error-correcting discrepancy loss, which significantly distinguish target samples and reduce their distinguished uncertainty. Then, we employ a feature adaptation metric structure to enhance the adaptation of ambiguous features via shallow layers of the backbone convolutional neural network and alignment loss, which automatically learns domain invariant features. The experimental results on four public datasets outperform other UDA methods of remote-sensing scene classification.

scholarly journals Few shot domain adaptation for in situ macromolecule structural classification in cryoelectron tomograms

2020 ◽  
Author(s):  
Liangyong Yu ◽  
Ran Li ◽  
Xiangrui Zeng ◽  
Hongyi Wang ◽  
Jie Jin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Spatial Organization ◽  
Domain Adaptation ◽  
Single Cells ◽  
Supplementary Information ◽  
Target Domain ◽  
Source Domain ◽  
Cellular Processes ◽  
Cross Domain

Abstract Motivation Cryoelectron tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at submolecular resolution. Such information is critical for the accurate understanding of cellular processes. However, subtomogram classification remains one of the major challenges for the systematic recognition and recovery of the macromolecule structures in cryo-ET because of imaging limits and data quantity. Recently, deep learning has significantly improved the throughput and accuracy of large-scale subtomogram classification. However, often it is difficult to get enough high-quality annotated subtomogram data for supervised training due to the enormous expense of labeling. To tackle this problem, it is beneficial to utilize another already annotated dataset to assist the training process. However, due to the discrepancy of image intensity distribution between source domain and target domain, the model trained on subtomograms in source domain may perform poorly in predicting subtomogram classes in the target domain. Results In this article, we adapt a few shot domain adaptation method for deep learning-based cross-domain subtomogram classification. The essential idea of our method consists of two parts: (i) take full advantage of the distribution of plentiful unlabeled target domain data, and (ii) exploit the correlation between the whole source domain dataset and few labeled target domain data. Experiments conducted on simulated and real datasets show that our method achieves significant improvement on cross domain subtomogram classification compared with baseline methods. Availability and implementation Software is available online https://github.com/xulabs/aitom. Supplementary information Supplementary data are available at Bioinformatics online.

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