To prevent noise amplification in GANs when training on complex, high-resolution images, you can follow the following techniques:
- Progressive Growing: Train the GAN progressively, starting from low-resolution images and gradually increasing the resolution. This prevents noise from being amplified at high resolutions.
- Spectral Normalization: Apply spectral normalization to the discriminator’s weights to stabilize training and prevent the model from overly amplifying noise in high-resolution images.
- Instance Normalization: Use instance normalization in the generator to reduce noise variations between training samples.
- Noise Regularization: Apply regularization to the noise (latent vector) during training to prevent the generator from exploiting it too much.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Progressive Growing: The generator starts with lower-resolution images and gradually increases the resolution, reducing noise amplification.
- Spectral Normalization: Stabilizes the discriminator by normalizing its weights, preventing the model from amplifying noise in high-resolution data.
- Instance Normalization: Applies normalization to reduce noise variations and stabilize the generator.
- Stable Training: Both generator and discriminator are regularized to ensure that high-frequency noise doesn't dominate the output.
Hence, by referring to the above you can prevent noise amplification in GANs when training on complex high-resolution images.
