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Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Open accessAbstract
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that have tried to either map representations between the two domains, or learn to extract features that are domain-invariant. In this work, we approach the problem in a new light by learning in an unsupervised manner a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.
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“Adversarial-based methods build a domain classifier to distinguish whether the features come from source domain or target domain. After being trained, the extractor could produce invariant feature from both source domain and target domain [68], [69], [70].”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [70]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
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Open Questions about Generative Adversarial Networks
Odena, A. (2019) · Distill · doi:10.23915/distill.00018 · Cited by 37
Hybrid adversarial network for unsupervised domain adaptation
Zhang, C., Zhao, Q., & Wang, Y. (2019) · Information Sciences · doi:10.1016/j.ins.2019.12.005 · Cited by 32
Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search
Pandey, P., Prathosh, A. P., Kyatham, V., Mishra, D., & Dastidar, T. R. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3009029 · Cited by 36
Unsupervised domain adaptation with self-attention for post-disaster building damage detection
Li, Y., Lin, C., Li, H., Hu, W., Dong, H., & Liu, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.005 · Cited by 34
Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data
Wang, Z., & Jing, J. (2020) · IEEE Access · doi:10.1109/access.2020.3021189 · Cited by 33
Pitteri, G., Bugeau, A., Ilić, S., & Lepetit, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69525-5_3 · Cited by 33
Speaker-Invariant Adversarial Domain Adaptation for Emotion Recognition
Yin, Y., Huang, B., Wu, Y., & Soleymani, M. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418813 · Cited by 34
Sock, J., Garcia-Hernando, G., Armagan, A., & Kim, T. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00039 · Cited by 37
Graphical Modeling for Multi-Source Domain Adaptation
Xu, M., Wang, H., & Ni, B. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3172372 · Cited by 33
Associative Domain AdaptationPreprint
Haeusser, P., Frerix, T., Mordvintsev, A., & Cremers, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00938 · Cited by 40
Category-Level Assignment for Cross-Domain Semantic Segmentation in Remote Sensing Images
Ni, H., Liu, Q., Guan, H., Tang, H., & Chanussot, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3271776 · Cited by 32
TUNet and domain adaptation based learning for joint optic disc and cup segmentation
Li, Z., Zhao, C., Han, Z., & Hong, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107209 · Cited by 33
A Deep Similarity Metric Method Based on Incomplete Data for Traffic Anomaly Detection in IoT
Xu, K., Song, B., & Sun, F. (2019) · Applied Sciences · doi:10.3390/app9010135 · Cited by 32
Inferring Latent Domains for Unsupervised Deep Domain Adaptation
Mancini, M., Porzi, L., Bulò, S. R., Barbara Caputo, B., & Ricci, E. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2933829 · Cited by 34
Deep Domain Adaptation With Differential Privacy
Wang, Q., Li, Z., Zou, Q., Zhao, L., & Wang, S. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2983254 · Cited by 33
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Jing, T., Xia, H., & Ding, Z. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413986 · Cited by 32
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Zhang, Y., Nie, S., Liang, S., & Liu, W. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3066903 · Cited by 32
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Zhang, C., Li, Z., Liu, J., Peng, P., Ye, Q., Lu, S., Huang, T., & Tian, Y. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3078141 · Cited by 34
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Wang, W., Zhang, J., Zhai, W., Cao, Y., & Tao, D. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3146017 · Cited by 33
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Kumar, V., Lal, R., Patil, H., & Chakraborty, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00416 · Cited by 32
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Wisdom, S., Tzinis, E., Erdoğan, H., Weiss, R. J., Wilson, K., & Hershey, J. R. (2020) · Cited by 31
Yu, F., Wu, X., Chen, J., & Duan, L. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2917867 · Cited by 30
Efficient Multi-Domain Learning by Covariance Normalization
Li, Y., & Vasconcelos, N. M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00557 · Cited by 33
Transfer Anomaly Detection by Inferring Latent Domain Representations
Kumagai, A., Iwata, T., & Fujiwara, Y. (2019) · neural information processing systems · Cited by 31
Consistent Embedded GAN for Image-to-Image Translation
Xiong, F., Wang, Q., & Gao, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2939654 · Cited by 31
Nirmal, S., Sowmya, V., & Soman, K. P. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-0146-3_78 · Cited by 31
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Li, R., Cao, W., Wu, S., & Wong, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3009853 · Cited by 32
Unsupervised Domain Adaptation for Plant Organ Counting
Ayalew, T. W., Ubbens, J. R., & Stavness, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_23 · Cited by 31
Constrained Domain Adaptation for Image Segmentation
Bateson, M., Dolz, J., Kervadec, H., Lombaert, H., & Ayed, I. B. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3067688 · Cited by 30
Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Fu, Y.-Q., Fu, Y.-W., Chen, J.-J., & Jiang, Y.-G. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3219237 · Cited by 102
Optimal Transport Guided Unsupervised Learning for Enhancing Low-Quality Retinal Images
Zhu, W., Qiu, P., Farazi, M., Nandakumar, K., Dumitrascu, O. M., & Wang, Y. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230719 · Cited by 31
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Self-supervised domain adaptation for machinery remaining useful life prediction
Xuan, Q. L., Munderloh, M., & Östermann, J. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110296 · Cited by 32
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Sweeney, C., Izatt, G., & Tedrake, R. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793820 · Cited by 30
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Pitteri, G., Ilić, S., & Lepetit, V. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00342 · Cited by 30
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Li, H., Wan, R., Wang, S., & Kot, A. C. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01364-5 · Cited by 31
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Li, Y., Yao, H., & Xu, C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3052354 · Cited by 30
Semi-Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Zhu, L., Yang, K., Zhang, M., Chan, L. L., Ng, T. K., & Ooi, B. C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3_37 · Cited by 30
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Hou, Y., & Zheng, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07514 · Cited by 49
Data augmentation using CycleGAN-based methods for automatic bridge crack detection
Li, B., Guo, H., & Wang, Z. (2024) · Structures · doi:10.1016/j.istruc.2024.106321 · Cited by 32
Prototype learning for adversarial domain adaptation
Fang, Y., Chen, C., Zhang, W., Wu, J., Zhang, Z., & Xie, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110653 · Cited by 30
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Chen, L., Lu, J.-W., Song, Z.-J., & Zhou, J. (2021) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2020.2975065 · Cited by 32
BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference
Park, S.-W., Huh, J., & Kim, J.-C. (2020) · Electronics · doi:10.3390/electronics9040688 · Cited by 30
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
Nguyen-Meidine, L. T., Granger, É., Kiran, M., Dolz, J., & Blais-Morin, L.-A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206989 · Cited by 29
Zhang, C., Tang, Y., Zhang, Z., Ding, L., Yang, X., & Zhang, W. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3047095 · Cited by 29
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Zhang, B., Chen, T., Wang, B., & Li, R. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3114550 · Cited by 29
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Yang, Y., Zhang, T., Li, G., Kim, T., & Wang, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.12.060 · Cited by 29
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Wu, Z., Wu, X., Zhang, X.-P. S., Wang, S., & Ju, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00759 · Cited by 29
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Zhou, S., Luo, P., Jain, D. K., Lan, X., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2930865 · Cited by 28
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Vishwakarma, P. K., & Jain, N. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-6289-8_52 · Cited by 28
High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks
Guo, B., Geng, G., Zhu, L., Shi, H., & Yu, Z. (2019) · Sensors · doi:10.3390/s19143075 · Cited by 28
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Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation
Ye, S., Wu, K., Zhou, M., Yang, Y., Tan, S. H., Xu, K., Song, J., Bao, C., & Ma, K. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01375 · Cited by 32
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Qian, M., Qiao, C., Lin, J., Guo, Z., Li, C., Leng, C., & Cheng, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-67070-2_14 · Cited by 28
Lin, C., Li, Y., Liu, Y., Wang, X., & Geng, S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3054869 · Cited by 30
Liang, C., Cheng, B., Xiao, B., He, C., Xunan, L., Jia, N., & Chen, J. (2021) · Remote Sensing · doi:10.3390/rs13061083 · Cited by 28
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Li, F., Lin, D., & Yu, T. (2020) · IEEE Access · doi:10.1109/access.2020.2992836 · Cited by 27
Chen, C., & Wang, G. (2020) · Applied intelligence (Boston) · doi:10.1007/s10489-020-01956-1 · Cited by 27
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Zhang, C., Wang, J., Yen, G. G., Zhao, C., Sun, Q., Tang, Y. S., Qian, F., & Kurths, J. (2020) · Patterns · doi:10.1016/j.patter.2020.100050 · Cited by 28
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Lu, G., Zhou, Z., Song, Y., Ren, K., & Yu, Y. H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014432 · Cited by 25
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Chen, Q., & Liu, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6629 · Cited by 25
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Yang, K., & Uhler, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11447 · Cited by 26
Ghaffari, R., Helfroush, M. S., Khosravi, A., Kazemi, K., Danyali, H., & Rutkowski, L. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101912 · Cited by 25
Deng, X., Zhu, Y., & Newsam, S. D. (2018) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3274895.3274969 · Cited by 24
Knowledge distillation methods for efficient unsupervised adaptation across multiple domains
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Park, S. J., Park, H. J., Kang, E. S., Ngo, B. H., Lee, H. S., & Cho, S. I. (2022) · IEEE Access · doi:10.1109/access.2022.3202190 · Cited by 25
IterDANet: Iterative Intra-Domain Adaptation for Semantic Segmentation of Remote Sensing Images
Cai, Y., Yang, Y., Shang, Y., Chen, Z., Shen, Z., & Yin, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3203040 · Cited by 24
Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation
Zhang, X., Chen, R., Li, A., Xiang, F., Qin, Y., Gu, J., Ling, Z., Liu, M., Zeng, P., Han, S., et al. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3235591 · Cited by 28
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
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Metadata
- Authors
- Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D.
- Affiliations
- Google (United Kingdom) (GB)
- Google (United States) (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 95-104
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2584009249
- Cited by
- 1,603
- References
- 51
- Citations by year
- 2026: 20 · 2025: 87 · 2024: 133 · 2023: 149 · 2022: 186 · 2021: 287 · 2020: 278 · 2019: 281 · 2018: 151 · 2017: 31
- FWCI
- 92.42
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised domain adaptation, generative adversarial networks, synthetic data, domain-invariant representation learning, image-to-image translation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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