Hello there,
I am currently working on a project that involves implementing Generative Adversarial Networks (GANs) for image generation, and I’m seeking some guidance from the community.
Specifically, I am looking for advice on the following aspects of GAN implementation:
Which GAN architectures have shown promising results for image generation tasks, especially in terms of generating high-quality images with fine details?
What are some best practices for training GANs to achieve stable training and avoid common issues like mode collapse?
How should I evaluate the performance of my GAN model, especially when it comes to assessing the quality and diversity of generated images?
Are there any specific considerations or preprocessing steps that are crucial for preparing a dataset for training a GAN model?
What are some common challenges faced during GAN training, and how can I effectively debug and troubleshoot these issues?
If you have any insights, resources, or personal experiences to share regarding these topics, I would greatly appreciate your input.
Also, if there are any specific tutorials, papers, or tools that you would recommend for someone new to GANs, please feel free to share them.
Thank you in advance for your help and assistance.