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Enhancing unsupervised domain adaptation by exploiting the conceptual consistency of multiple self-supervised tasks

Science China(Information Sciences) 2023年第4期66卷 126-139页

作者： Hui SUN Ming LI National Key Laboratory for Novel Software Technology Nanjing University

Unsupervised domain adaptation(UDA) aims to transfer the knowledge from a label-rich source domain to an unlabeled target domain. Current approaches mainly focus on aligning the target domain’s data distribution with the source domain, but lacking attention to allowing target data to guide which features need to be captured. Source data dominates feature extraction. As a result, target embedding would lose some vital discriminative features and limit UDA in complex tasks. In this paper, we argue that utilizing auxiliary tasks to capture target-intrinsic patterns, which do not depend on source supervision, could further enhance UDA. Multiple auxiliary tasks understand the instance concept from different perspectives.Our findings show that exploiting the conceptual consistency of multiple auxiliary tasks to characterize the common part of these various understandings should reveal the target’s hidden ground truth. Furthermore, we propose a novel method named multiple self-supervision conceptual consistency domain adaptation(MSCC). Experiments and analysis on benchmark datasets show the effectiveness of our idea and method.

关键词： deep learning transfer learning domain adaptation domain shift self-supervised

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Dual collaboration for decentralized multi-source domain adaptation

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1780-1794页

作者： Yikang WEI Yahong HAN College of Intelligence and Computing Tianjin UniversityTianjin 300350China Tianjin Key Lab of Machine Learning Tianjin UniversityTianjin 300350China

The goal of decentralized multi-source domain adaptation is to conduct unsupervised multi-source domain adaptation in a data decentralization scenario. The challenge of data decentralization is that the source domains and target domain lack cross-domain collaboration during training. On the unlabeled target domain, the target model needs to transfer supervision knowledge with the collaboration of source models, while the domain gap will lead to limited adaptation performance from source models. On the labeled source domain, the source model tends to overfit its domain data in the data decentralization scenario, which leads to the negative transfer problem. For these challenges, we propose dual collaboration for decentralized multi-source domain adaptation by training and aggregating the local source models and local target model in collaboration with each other. On the target domain, we train the local target model by distilling supervision knowledge and fully using the unlabeled target domain data to alleviate the domain shift problem with the collaboration of local source models. On the source domain, we regularize the local source models in collaboration with the local target model to overcome the negative transfer problem. This forms a dual collaboration between the decentralized source domains and target domain, which improves the domain adaptation performance under the data decentralization scenario. Extensive experiments indicate that our method outperforms the state-of-the-art methods by a large margin on standard multi-source domain adaptation datasets.

关键词： Multi-source domain adaptation Data decentralization domain shift Negative transfer

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Source-Free Unsupervised domain Adaptation with Sample Transport Learning

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Journal of Computer Science & Technology 2021年第3期36卷 606-616页

作者： Qing Tian Chuang Ma Feng-Yuan Zhang Shun Peng Hui Xue School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Southeast UniversityNanjing 211189China

Unsupervised domain adaptation(UDA)has achieved great success in handling cross-domain machine learning applications.It typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source domain.For this purpose,the minimization of the marginal distribution divergence and conditional distribution divergence between the source and the target domain is widely adopted in existing work.Nevertheless,for the sake of privacy preservation,the source domain is usually not provided with training data but trained predictor(e.g.,classifier).This incurs the above studies infeasible because the marginal and conditional distributions of the source domain are incalculable.To this end,this article proposes a source-free UDA which jointly models domain adaptation and sample transport learning,namely Sample Transport domain Adaptation(STDA).Specifically,STDA constructs the pseudo source domain according to the aggregated decision boundaries of multiple source classifiers made on the target domain.Then,it refines the pseudo source domain by augmenting it through transporting those target samples with high confidence,and consequently generates labels for the target domain.We train the STDA model by performing domain adaptation with sample transport between the above steps in alternating manner,and eventually achieve knowledge adaptation to the target domain and attain confident labels for it.Finally,evaluation results have validated effectiveness and superiority of the proposed method.

关键词： unsupervised domain adaptation domain shift sample transport pseudo source domain

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