Research map / Label-efficient and robust learning / Domain adaptation and generalization
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adversarial Fourier amplitude generation with supervised MCD | AGFA · PACS | accuracy: 88.1 -> 89.3 | Helped |
| Use supervised MCD with labels induced from source phase information (ablation) | AGFA · PACS | accuracy: 87.95 -> 89.30 | Helped |
| Add post-synthesis amplitude mixup (ablation) | AGFA · PACS | accuracy: 87.25 -> 89.30 | Helped |
| Add SWAD model averaging (ablation) | AGFA · PACS | accuracy: 84.97 -> 89.30 | Helped |
| Generate Fourier amplitudes rather than full images in pixel space (ablation) | AGFA · PACS | accuracy: 87.95 -> 89.30 | Helped |
Closest papers by meaning
- A Fourier-Based Framework for Domain Generalization (CVPR 2021 · similarity 0.91)
- f-Domain Adversarial Learning: Theory and Algorithms (ICML 2021 · similarity 0.89)
- Adversarial Discriminative Domain Adaptation (CVPR 2017 · similarity 0.88)
- Adversarial Domain Adaptation with Domain Mixup (AAAI 2020 · similarity 0.88)
- Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization (CVPR 2025 · similarity 0.87)
- DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain (NeurIPS 2024 · similarity 0.87)
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