Domain-Adversarial Training of Neural Networks

Ganin, Yaroslav; Ustinova, Evgeniya; Ajakan, Hana; Germain, Pascal; Larochelle, Hugo; Laviolette, François; Marchand, Mario; Lempitsky, Victor

doi:10.1007/978-3-319-58347-1_10

Cited by 8,318Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsGenerative Adversarial Networks and Image Synthesis

Domain-Adversarial Training of Neural Networks

Ganin, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, François, Marchand, Mario, Lempitsky, Victor·2017·Advances in Computer Vision and Pattern Recognition

DOI 10.1007/978-3-319-58347-1_10

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Domain-adversarial learning is applied to spacecraft pose estimation to bridge the domain gap.
“This technique [171] is applied to spacecraft pose estimation [20] to bridge the domain gap.”
From A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects (2023) · cites this paper as [171]
Three common methods for domain adaptation are discrepancy-based, adversarial-based, and reconstruction-based methods.
“From the literature, we summarize three common methods for domain adaptation regardless of their tasks. They are discrepancy-based , adversarial-based , and reconstructionbased methods, which can be also used crossly.”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [68]
Other applications of DRL and sim-to-real transfer in robotics include control of a plasma jet, tactile sensing, and multiagent manipulation.
“Some other applications of DRL and sim-to-real transfer in robotics that have emerged over the past years are the control of a plasma jet [32], tactile sensing [43], or multiagent manipulation [44].”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [32]

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Authors: Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V.
Affiliations: Skolkovo Institute of Science and Technology (RU)
Université Laval (CA)
Institut national de recherche en sciences et technologies du numérique (FR)
Twitter (United States) (US)
Corresponding author: Ganin, Yaroslav
ORCID: Germain, Pascal
Laviolette, François
Marchand, Mario
Lempitsky, Victor
Venue: Advances in Computer Vision and Pattern Recognition
Publisher: Springer International Publishing
Published: 2017
Pages: 189-209
ISSN: 2191-6586 (ISSN-L)
2191-6594
DOI: 10.1007/978-3-319-58347-1_10
OpenAlex: W1731081199
Cited by: 8,318
References: 10
Citations by year: 2026: 260 · 2025: 910 · 2024: 1069 · 2023: 1172 · 2022: 905 · 2021: 1296 · 2020: 1126 · 2019: 823 · 2018: 442 · 2017: 260 · 2016: 48 · 2015: 1 · 2014: 1 · 2013: 3 · 2012: 1
FWCI: 1094.52
Type: book chapter
Language: en
Open access: closed
Licence: www.springer.com/tdm (unspecified)
Locations: Advances in computer vision and pattern recognition (publishedVersion)
Funders: Chinese University of Hong Kong
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords: domain adaptation, domain adversarial neural networks, gradient reversal layer, representation learning, distribution shift, image classification, person re-identification, descriptor learning

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