How can I resolve label inconsistency when using GANs for data generation

Question

With the help of code, can you tell me how I can resolve label inconsistency when using GANs for data generation?

Answer 1

To resolve label inconsistency when using GANs for data generation, ensure the discriminator and generator are trained with consistent label schemes. You can use label smoothing and one-sided label flipping as strategies to stabilize training.

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Label Smoothing: Softens real labels to improve stability (e.g., use 0.9 instead of 1.0).
• One-Sided Label Flipping: Introduces small noise in labels (e.g., flipping real to fake labels with low probability).
• Consistent Label Usage: Ensure real/fake labels are consistent across generator and discriminator training.

Hence, this helps mitigate label inconsistency and improves GAN training stability.