The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Domain Generalization with Adversarial Feature Learning
Abstract
In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data. We present a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization. To be specific, we extend adversarial autoencoders by imposing the Maximum Mean Discrepancy (MMD) measure to align the distributions among different domains, and matching the aligned distribution to an arbitrary prior distribution via adversarial feature learning. In this way, the learned feature representation is supposed to be universal to the seen source domains because of the MMD regularization, and is expected to generalize well on the target domain because of the introduction of the prior distribution. We proposed an algorithm to jointly train different components of our proposed framework. Extensive experiments on various vision tasks demonstrate that our proposed framework can learn better generalized features for the unseen target domain compared with state-of-the-art domain generalization methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Self-citationThe pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847
Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359
Deep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285
Return of Frustratingly Easy Domain Adaptation
Sun, B., Feng, J., & Saenko, K. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10306 · Cited by 2,004
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Research works printed but not linked: 29
- xu, 2014, Exploiting low-rank structure from latent domains for domain generalization, EC-CV
- weinland, 2006, Free viewpoint action recognition using motion history volumes, Computer Vision and Image Understanding, 104, 249, 10.1016/j.cviu.2006.07.013 · DOI · Paper page
- sriperumbudur, 2009, Kernel choice and classifia-bility for RKHS embeddings of probability distributions, NIPS, 1750
- smola, 2007, A hilbert space embedding for distributions, ALT, 13
- 10.1109/TIP.2018.2808768 · DOI
- ganin, 2016, Domain-adversarial training of neural networks, Journal of Machine Learning Research, 17, 1
- yang, 2013, Multi-view discriminant transfer learning, IJCAI
- 10.1109/ICCV.2015.293 · DOI · Paper page
- ghifary, 2016, Deep reconstruction-classification networks for unsupervised domain adaptation, ECCV
- gong, 2012, Geodesic flow kernel for unsupervised domain adaptation, CVPR
- goodfellow, 2014, Generative adversarial nets, NIPS
- gretton, 2006, A kernel method for the two-sample-problem, NIPS
- gretton, 2012, Optimal kernel choice for large-scale two-sample tests, NIPS
- lecun, 1998, The MNIST Database of Handwritten Digits
- 10.1109/ICCV.2017.591 · DOI · Paper page
- 10.1145/1553374.1553411 · DOI · Paper page
- 10.1109/TNN.2010.2091281 · DOI · Paper page
- 10.1007/s11263-007-0090-8 · DOI · Paper page
- dumoulin, 2016, Adversarially learned inference
- 10.1109/ICCV.2013.208 · DOI · Paper page
- 10.1109/CVPR.2010.5540221 · DOI · Paper page
- 10.1109/ICCV.2013.368 · DOI · Paper page
- 10.1109/TPAMI.2013.50 · DOI · Paper page
- li, 2015, Generative moment matching networks, ICML
- long, 2016, Unsupervised domain adaptation with residual transfer networks, NIPS
- long, 2015, Learning transferable features with deep adaptation networks, ICML
- mao, 2016, Least squares generative adversarial networks
- makhzani, 2016, Adversarial autoencoders, ICLR Workshop
- 10.1109/ICCV.2017.609 · DOI · Paper page
Cited by
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315
Domain adaptation for medical image analysis: A survey
Guan, H., & Liu, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3117407 · Cited by 1,057
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046
A Fourier-based Framework for Domain Generalization
Xu, Q., Zhang, R., Zhang, Y., Wang, Y., & Tian, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01415 · Cited by 561
Episodic Training for Domain Generalization
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z., & Hospedales, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00153 · Cited by 435
Metadata
- Authors
- Li, H., Pan, S. J., Wang, S., & Kot, A. C.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5400-5409
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798658180
- Cited by
- 1,213
- References
- 11
- FWCI
- 32.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Domain Generalization with Adversarial Feature Learning. https://app.cochat.ai/papers/10.1109/cvpr.2018.00566
- The paper
- Li, H., Pan, S. J., Wang, S., & Kot, A. C. (2018). Domain Generalization with Adversarial Feature Learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00566
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
