How do you overcome local minima in GAN training for realistic text generation

Question

Can i know How do you overcome local minima in GAN training for realistic text generation?

Answer 1

Overcome local minima in GAN training for realistic text generation by using reinforcement learning, curriculum learning, and gradient penalty stabilization. Here is the code snippet you can refer to:

In the above code, we are using the following key approaches

• Reinforcement Learning (RL) Reward: Uses diversity-based reward function to escape local minima.

• Pre-trained GPT Generator: Leverages GPT-2 for high-quality text generation.

• Gradient Stabilization: Uses Adam optimizer with tuned betas to prevent training collapse.

• Curriculum Learning Ready: Can be expanded with difficulty progression for smoother convergence.

Hence, by integrating reinforcement learning rewards and stabilization techniques, GAN-based text generation can avoid local minima and produce more realistic outputs.