What does the "two-time-scale update rule" (TTUR) in GANs achieve?

What does the "two-time-scale update rule" (TTUR) in GANs achieve?

• A. It allows simultaneous updates for both Generator and Discriminator
• B. It adjusts the learning rates of the Generator and Discriminator independently for stability
• C. It simplifies the architecture of GANs for easier training
• D. It eliminates the need for a Discriminator in GANs

Answer: (B)

The "two-time-scale update rule" (TTUR) in GANs is particularly effective because it allows for independent adjustment of the learning rates for both the Generator and the Discriminator. This independent tuning is crucial for stability in training, as it addresses the asymmetry in learning performance between the two networks. The Generator often needs to be trained at a different pace compared to the Discriminator to maintain a balance where both can improve effectively. By having distinct learning rates, TTUR helps to prevent scenarios where one model could overpower the other too quickly, leading to instability or mode collapse in the training process. Ultimately, this approach enhances the overall robustness and efficiency of training GANs.

The "two-time-scale update rule" (TTUR) in GANs is particularly effective because it allows for independent adjustment of the learning rates for both the Generator and the Discriminator. This independent tuning is crucial for stability in training, as it addresses the asymmetry in learning performance between the two networks. The Generator often needs to be trained at a different pace compared to the Discriminator to maintain a balance where both can improve effectively. By having distinct learning rates, TTUR helps to prevent scenarios where one model could overpower the other too quickly, leading to instability or mode collapse in the training process. Ultimately, this approach enhances the overall robustness and efficiency of training GANs.