How do you implement GANs for dataset augmentation in low-data scenarios

Question

With the help of coding, can you implement GANs to augment datasets in low-data scenarios?

Answer 1

In order to implement GANs for dataset augmentation in low-data scenarios, here is the code you can refer to:

• Define the Generator and Discriminator: You can use simple GAN components for image generation.
• Train the GAN: You can generate synthetic data using the trained generator.
• Augment Dataset: You can use the trained generator to produce new samples for your dataset.

In the code, we can train the GAN to generate synthetic samples, which you can then use to augment your dataset for tasks like classification or regression.

Hence, by using the above techniques, you can implement GANs for dataset augmentation in low-data scenarios, here is the code you can refer to: