To improve generator performance in GANs when using small datasets, you can follow the following key points:
- Data Augmentation: Use transformations like rotation, flipping, scaling, and color jitter to artificially increase the dataset size.
- Transfer Learning: Use pre-trained models (e.g., from image classification tasks) to initialize the generator and fine-tune it for your task.
- Regularization: Apply techniques like L2 regularization or dropout to prevent overfitting.
- Use a Conditional GAN: Use class labels as additional input to the generator to capture the distribution of data better.
- Use Spectral Normalization: Stabilize training and improve convergence with spectral normalization.
Here is the code snippet you can refer to:
In the above code, we are using the following:
- Data Augmentation: Apply random transformations to increase data variability artificially.
- Transfer Learning: Use pre-trained models for feature extraction in the generator.
- Regularization: Add dropout or L2 regularization to prevent overfitting.
- Spectral Normalization: Apply to the discriminator and generator for stability.
Hence, by leveraging data augmentation, transfer learning, and regularization, you can improve the generator’s performance even when working with small datasets.
