The DOI matches its Crossref record, with a small difference in title or year; 11 of the 11 references it cites were found in a registry or an index.
Domain-Adversarial Training of Neural Networks
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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
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Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
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Pooch, E. H. P., Ballester, P. L., & Barros, R. C. (2019) · arXiv · doi:10.48550/arxiv.1909.01940 · Cited by 32
Zhuo, T. Y., Li, Z., Huang, Y., Shiri, F., Wang, W., Haffari, G., & Li, Y.-F. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.77 · Cited by 31
An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases
Dong, D., Fu, G., Li, J., Pei, Y., & Chen, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118016 · Cited by 30
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
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Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 27
Progressive modality cooperation for multi-modality domain adaptation
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Learning Universal Authorship Representations
Rivera-Soto, R. A., Miano, O. E., Ordonez, J., Chen, B. Y., Khan, A., Bishop, M., & Andrews, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.70 · Cited by 25
“In future work, it would be interesting to attempt to explicitly disentangle the learned representations into content and style, for example, by using domain adversarial training objectives (Ganin et al., 2016; Bischoff et al., 2020).”
BackgroundStylianou, N., Razis, G., Goulis, D. G., & Vlahavas, I. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101949 · Cited by 22
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Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets
Kang, H., Huang, Q., & Zhang, Q. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550285 · Cited by 16
Spurious Correlations in Reference-Free Evaluation of Text Generation
Durmus, E., Ladhak, F., & Hashimoto, T. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.102 · Cited by 16
Meta-Transfer Learning-Based Handover Optimization for V2N Communication
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Question Generation With Doubly Adversarial Nets
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Deep Learning in NLP for Anomalous HTTP Requests Detection
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Toward Real-World Voice Disorder Classification
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Towards Recognizing Unseen Categories in Unseen Domains
Mancini, M., Akata, Z., Ricci, E., & Caputo, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58592-1_28 · Cited by 9
Cross-domain person re-identification using Dual Generation Learning in camera sensor networks
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Adversarial Unsupervised Representation Learning for Activity Time-Series
Aggarwal, K., Joty, S., Fernandez-Luque, L., & Srivastava, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301834
Zheng, Y., Schupp, J., Adams, T., Clair, G., Justet, A., Ahangari, F., Yan, X., Hansen, P., Carlon, M., Cortesi, E., et al. (2023) · doi:10.21203/rs.3.rs-3676579/v1 · Cited by 34
Metadata
- 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
- 11
- Citations by year
- 2026: 260 · 2025: 910 · 2024: 1069 · 2023: 1172 · 2022: 905 · 2021: 1296 · 2020: 1126 · 2019: 823 · 2018: 442 · 2017: 260
- 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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