To use adversarial training to mitigate image artifact generation in generative image models, you can follow the following steps below:
- Discriminator Improvements: Use a discriminator to penalize artifacts by focusing on high-frequency details (e.g., PatchGAN or multi-scale discriminators).
- Feature-Level Losses: Combine adversarial loss with feature-matching or perceptual loss to enhance realism and reduce artifacts.
- Regularization: Apply gradient penalty or spectral normalization to stabilize training and minimize artifacts.
- Data Augmentation: Introduce diverse augmentations to improve generalization and reduce overfitting to artifacts.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Adversarial Loss: Helps the generator reduce artifacts by fooling the discriminator.
- Feature Matching (Perceptual Loss): Preserves structural and texture details to enhance image quality.
- PatchGAN Discriminator: Focuses on local image features to catch artifacts.
- Regularization: Techniques like spectral normalization stabilize adversarial training.
Hence, by referring to the above, you can use adversarial training to mitigate issues with image artifact generation in Generative Image Models.
