Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
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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 features come from source or target domain, and after training the extractor can produce invariant features from both domains.
“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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- 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
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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.
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