Papers › Adversarial Bayesian Augmentation for Single-Source Domain Generalization
Adversarial Bayesian Augmentation for Single-Source Domain Generalization
Sheng Cheng, Tejas Gokhale, Yezhou Yang
Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exist in many real-world settings. As such data augmentation is a critical component of domain generalization methods that seek to address this problem. We present Adversarial Bayesian Augmentation (ABA), a novel algorithm that learns to generate image augmentations in the challenging single-source domain generalization setting. ABA draws on the strengths of adversarial learning and Bayesian neural networks to guide the generation of diverse data augmentations -- these synthesized image domains aid the classifier in generalizing to unseen domains. We demonstrate the strength of ABA on several types of domain shift including style shift, subpopulation shift, and shift in the medical imaging setting. ABA outperforms all previous state-of-the-art methods, including pre-specified augmentations, pixel-based and convolutional-based augmentations.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Photo to Rest Generalization | PACS | ABA (ResNet18) | Accuracy | 59.04 | #5 of 8 | Archive leaderboard | report |
| Single-Source Domain Generalization | Digits-five | ABA (LeNet) | Accuracy | 76.72 | #6 of 7 | Archive leaderboard | report |
| Single-Source Domain Generalization | PACS | ABA (ResNet18) | Accuracy | 66.36 | #7 of 10 | 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.
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