What methods could you apply to prevent underfitting when training GANs on small complex datasets

Question

With the help of python programming can you tell me What methods could you apply to prevent underfitting when training GANs on small, complex datasets?

Answer 1

To prevent underfitting when training GANs on small, complex datasets, You can follow the following steps:

• Data Augmentation: Apply transformations like rotations, flips, or color jitter to increase dataset diversity.
• Feature Matching Loss: Improve stability by aligning features between real and generated data.
• Pretrained Models: Use transfer learning by initializing the generator or discriminator with pre-trained weights.
• Regularization: Apply spectral normalization or weight decay to stabilize training.
• Progressive Growing: Train the GAN starting from lower resolutions and gradually increasing complexity.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Data Augmentation: Expands dataset diversity to reduce overfitting.
• Spectral Normalization: Stabilizes training and prevents exploding gradients.
• Transfer Learning: Leverages pre-trained weights to boost performance on small datasets.
• Feature Matching Loss: Aligns intermediate feature representations for better sample quality.

Hence, by referring to the above, you can prevent underfitting when training GANs on small, complex datasets