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IEEE Transactions on Neural Networks and Learning Systems, 2023
Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization.
Sentao Chen +3 more
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Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization.
Sentao Chen +3 more
openaire +2 more sources
Adaptive Component Embedding for Domain Adaptation
IEEE Transactions on Cybernetics, 2021Domain adaptation is suitable for transferring knowledge learned from one domain to a different but related domain. Considering the substantially large domain discrepancies, learning a more generalized feature representation is crucial for domain adaptation.
Mengmeng Jing +5 more
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Domain Adaptation Multitask Optimization
IEEE Transactions on Cybernetics, 2023Multitask optimization (MTO) is a new optimization paradigm that leverages useful information contained in multiple tasks to help solve each other. It attracts increasing attention in recent years and gains significant performance improvements. However, the solutions of distinct tasks usually obey different distributions.
Xiaoling Wang +4 more
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DACH: Domain Adaptation Without Domain Information
IEEE Transactions on Neural Networks and Learning Systems, 2020Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video feeds from multiple surveillance cameras.
Ruichu Cai +4 more
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Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation
ACM Transactions on Multimedia Computing, Communications, and Applications, 2022Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain.
Jinfeng Li +5 more
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Subspace Distribution Adaptation Frameworks for Domain Adaptation
IEEE Transactions on Neural Networks and Learning Systems, 2020Domain adaptation tries to adapt a model trained from a source domain to a different but related target domain. Currently, prevailing methods for domain adaptation rely on either instance reweighting or feature transformation. Unfortunately, instance reweighting has difficulty in estimating the sample weights as the dimension increases, whereas feature
Sentao Chen +4 more
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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
IEEE Transactions on Image ProcessingUnsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task ...
Duo Peng +5 more
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IEEE Transactions on Circuits and Systems for Video Technology, 2017
In many real-world applications, labeled data are either expensive or too scarce to be used to train an accurate classifier. Therefore, it is worth exploring and often essential to make full use of existing resources. Domain adaptation is one of the most promising techniques of leveraging an existing well-labeled source domain and a limited labeled ...
Jingjing Li, Yue Wu, Ke Lu
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In many real-world applications, labeled data are either expensive or too scarce to be used to train an accurate classifier. Therefore, it is worth exploring and often essential to make full use of existing resources. Domain adaptation is one of the most promising techniques of leveraging an existing well-labeled source domain and a limited labeled ...
Jingjing Li, Yue Wu, Ke Lu
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2020
Supervised learning algorithms require sufficient amount of labeled training data for learning robust prediction models. The field of Transfer Learning (TL) (also known as knowledge transfer) deals with utilizing knowledge from data-rich auxiliary domains to learn a reliable predictor for the domain of interest. This chapter presents a condensed review
Sanatan Sukhija +1 more
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Supervised learning algorithms require sufficient amount of labeled training data for learning robust prediction models. The field of Transfer Learning (TL) (also known as knowledge transfer) deals with utilizing knowledge from data-rich auxiliary domains to learn a reliable predictor for the domain of interest. This chapter presents a condensed review
Sanatan Sukhija +1 more
openaire +1 more source