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Domain Adversarial Training: A Game Perspective
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace GD-NM with RK2 | f-DAL Pearson with Implicit Alignment · VisDA-2017 | transfer performance: +3.5% | Helped |
| Replace GD-NM with RK2 | f-DAL Pearson with Implicit Alignment · VisDA-2017 | training iterations: 10.5K vs 29.5K | Helped |
Closest papers by meaning
- f-Domain Adversarial Learning: Theory and Algorithms (ICML 2021 · similarity 0.91)
- You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle (NeurIPS 2019 · similarity 0.89)
- Improving the Generalization of Adversarial Training with Domain Adaptation (ICLR 2019 · similarity 0.89)
- AT-Field: Rethinking the Games in Adversarial Training (AAAI 2026 · similarity 0.89)
- Finding Mixed Nash Equilibria of Generative Adversarial Networks (ICML 2019 · similarity 0.88)
- Adversarial Training Should Be Cast as a Non-Zero-Sum Game (ICLR 2024 · similarity 0.87)
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