Domain-Adversarial Training of Neural Networks
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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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BackgroundMethodA review of recent advances and strategies in transfer learning
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“…n-grams (Madigan et al., 2005b, Menon and Choi, 2011), while recent approaches highlight the importance of training more powerful transformer-based models (Rivera-Soto et al., 2021) and techniques such as adversarial training (Ben-David et al., 2010, Ganin et al., 2016, Li et al., 2017).”
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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
- Venue
- Advances in Computer Vision and Pattern Recognition
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 189-209
- ISSN
- 2191-6586 (ISSN-L)
- 2191-6594
- 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
- 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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