What technique can be used to mitigate mode collapse?

What technique can be used to mitigate mode collapse?

• A. Increasing the training dataset size
• B. Using a single loss function for training
• C. Adding noise to the input of the Generator or using various loss functions
• D. Setting a fixed number of iterations for training

Answer: (C)

Mitigating mode collapse in Generative Adversarial Networks (GANs) involves addressing the tendency of the generator to produce a limited variety of outputs despite having a potentially vast input distribution. The correct approach is to add noise to the input of the generator or to utilize various loss functions. When noise is introduced to the generator's input, it helps to ensure that the model does not settle on producing a narrow set of outputs. This randomness encourages the generator to explore different regions of the input space, thereby promoting diversity in the generated samples. Additionally, employing various loss functions can create a more complex training landscape that encourages the generator and discriminator to adapt to one another more effectively and maintain variety in the outputs generated. Increasing the training dataset size or setting fixed iterations may improve training overall but doesn’t specifically target mode collapse. Similarly, using a single loss function may fail to introduce the required diversity or complexity in the training dynamic. Thus, the use of added noise or multiple loss functions is a strategically effective method to combat mode collapse and enhance the performance of GANs.

Mitigating mode collapse in Generative Adversarial Networks (GANs) involves addressing the tendency of the generator to produce a limited variety of outputs despite having a potentially vast input distribution. The correct approach is to add noise to the input of the generator or to utilize various loss functions.

When noise is introduced to the generator's input, it helps to ensure that the model does not settle on producing a narrow set of outputs. This randomness encourages the generator to explore different regions of the input space, thereby promoting diversity in the generated samples. Additionally, employing various loss functions can create a more complex training landscape that encourages the generator and discriminator to adapt to one another more effectively and maintain variety in the outputs generated.

Increasing the training dataset size or setting fixed iterations may improve training overall but doesn’t specifically target mode collapse. Similarly, using a single loss function may fail to introduce the required diversity or complexity in the training dynamic. Thus, the use of added noise or multiple loss functions is a strategically effective method to combat mode collapse and enhance the performance of GANs.