Research map / Optimization / Nonconvex and smooth optimization
Minimax Optimization with Smooth Algorithmic Adversaries
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Differentiate through the discriminator algorithm using the total gradient instead of the partial gradient | Minimax Optimization with Smooth Algorithmic Adversaries · Dirac-GAN | convergence speed | Helped |
| Use the total gradient instead of the partial gradient | Minimax Optimization with Smooth Algorithmic Adversaries · MNIST adversarial training | robustness | Helped |
| Train with an adversarial attack rather than standard training | Minimax Optimization with Smooth Algorithmic Adversaries · MNIST adversarial training | robustness | Helped |
| Train against one gradient-ascent attack and evaluate against attacks with different steps, learning rates, or algorithms | Minimax Optimization with Smooth Algorithmic Adversaries · MNIST adversarial training | test accuracy | Helped |
| Reinitialize the discriminator for each interaction and optimize through its algorithm | Minimax Optimization with Smooth Algorithmic Adversaries · Dirac-GAN | training stability | Helped |
| Train the generator using the smooth-adversary framework | Minimax Optimization with Smooth Algorithmic Adversaries · Mixture of Gaussians | mode recovery | Helped |
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