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Review of Studies on Domain Adaptation [PDF]
Jisuanji gongcheng, 2021 Classical machine learning algorithms assume that the training and testing instances share the same input feature space and data distribution.In many real-world applications, however, this assumption cannot be satisfied, resulting in the failure of the ...
LI Jingjing, MENG Lichao, ZHANG Ke, LU Ke, SHEN Hengtao
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Optimal Transport for Domain Adaptation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own ...
Courty, Nicolas +3 more
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Invertible Autoencoder for Domain Adaptation
Computation, 2019 The unsupervised image-to-image translation aims at finding a mapping between the source ( A ) and target ( B ) image domains, where in many applications aligned image pairs are not available at training.
Yunfei Teng, Anna Choromanska
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Deep adversarial domain adaptation network
International Journal of Advanced Robotic Systems, 2020 The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning.
Lan Wu +3 more
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Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization
Complexity, 2018 Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances
Yiwei He +3 more
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C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation
Sensors, 2020 Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain.
Han Sun +5 more
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Domain Adaptation: Challenges, Methods, Datasets, and Applications
IEEE Access, 2023 Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well on another set of data (target domain), which is different but has similar properties as the source domain.
Peeyush Singhal +3 more
semanticscholar +1 more source
Age differences in fMRI adaptation for sound identity and location [PDF]
, 2011 We explored age differences in auditory perception by measuring fMRI adaptation of brain activity to repetitions of sound identity (what) and location (where), using meaningful environmental sounds. In one condition, both sound identity and location were
Cheryl eGrady +6 more
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A framework for self-supervised federated domain adaptation
EURASIP Journal on Wireless Communications and Networking, 2022 Unsupervised federated domain adaptation uses the knowledge from several distributed unlabelled source domains to complete the learning on the unlabelled target domain.
Bin Wang +5 more
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Feature-Level Domain Adaptation [PDF]
, 2015 Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (FLDA), that models the dependence between
Kouw, Wouter M. +3 more
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