Papers › Deep Domain Generalization via Conditional Invariant Adversarial Networks
Deep Domain Generalization via Conditional Invariant Adversarial Networks
Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, DaCheng Tao
Domain generalization aims to learn a classification model from multiple source domains and generalize it to unseen target domains. A critical problem in domain generalization involves learning domain-invariant representations. Let X and Y denote the features and the labels, respectively. Under the assumption that the conditional distribution P(Y|X) remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation T(X) by minimizing the discrepancy of the marginal distribution P(T(X)). However, such an assumption of stable P(Y|X) does not necessarily hold in practice. In addition, the representation learning function T(X) is usually constrained to a simple linear transformation or shallow networks. To address the above two drawbacks, we propose an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning. The domain-invariance property is guaranteed through a conditional invariant adversarial network that can learn domain-invariant representations w.r.t. the joint distribution P(T(X),Y) if the target domain data are not severely class unbalanced. We perform various experiments to demonstrate the effectiveness of the proposed method.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | PACS | CDANN (Resnet-50, DomainBed) | Average Accuracy | 82.6 | #76 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | CIDDG (Alexnet) | Average Accuracy | 68.88 | #128 of 133 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections