What techniques do you use to implement adversarial training in GANs to improve image quality

Question

Can you name a few techniques used to implement adversarial training in GANs to improve image quality?

Answer 1

You can refer to the below techniques to implement adversarial training in GANs to improve image quality:

• Label Smoothing: Smooth the labels to make the discriminator less confident, leading to the prevention of overfitting.
• Feature Matching: You can also have the generator match intermediate features from the discriminator to produce more realistic images.
• Mini-Batch Discrimination: Add diversity in generated images by having the discriminator analyze differences within a mini-batch.
• Gradient Penalty: You can apply a gradient penalty (like WGAN-GP) to encourage a smoother and more stable discriminator gradient.
• Spectral Normalization: You can regularize the discriminator by normalizing its weights, stabilizing training, and improving quality.

By the above techniques, you can enhance stability, prevent model collapse, and produce high-quality images.