Research map / Label-efficient and robust learning / Domain adaptation and generalization
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add adversarial learning to the classifier and reconstruction-loss setup (ablation) | DupGAN · MNIST →USPS | rank-1 accuracy: 94.57 -> 96.01 | Helped |
| Add adversarial learning to the classifier and reconstruction-loss setup (ablation) | DupGAN · SVHN →MNIST | rank-1 accuracy: 68.30 -> 92.46 | Helped |
| Add the discriminators (ablation) | DupGAN · MNIST →USPS | rank-1 accuracy: 93.82 -> 96.01 | Helped |
| Add the discriminators (ablation) | DupGAN · SVHN →MNIST | rank-1 accuracy: 67.43 -> 92.46 | Helped |
| Add adversarial learning and the generator to the classifier-only setup (ablation) | DupGAN · MNIST →USPS | rank-1 accuracy: 93.32 -> 96.01 | Helped |
| Add adversarial learning and the generator to the classifier-only setup (ablation) | DupGAN · SVHN →MNIST | rank-1 accuracy: 60.18 -> 92.46 | Helped |
Closest papers by meaning
- Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks (CVPR 2017 · similarity 0.91)
- Adversarial Discriminative Domain Adaptation (CVPR 2017 · similarity 0.91)
- Generate to Adapt: Aligning Domains Using Generative Adversarial Networks (CVPR 2018 · similarity 0.90)
- HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks (NeurIPS 2022 · similarity 0.90)
- Adversarial Feature Augmentation for Unsupervised Domain Adaptation (CVPR 2018 · similarity 0.90)
- Discriminative Adversarial Domain Adaptation (AAAI 2020 · similarity 0.89)
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