Hands-On Generative Adversarial Networks with PyTorch

Training StackGAN++ to Generate Images with Better Quality

Explore the step-by-step process to train StackGAN++ on the CUB-200-2011 dataset, including setup, code modifications for PyTorch, and evaluating text-to-image models to generate high-quality images from descriptions.

We'll cover the following...

Steps to train StackGAN++

1.Getting Started

2.Generative Adversarial Networks Fundamentals

3.Best Practices for Model Design and Training

4.Building Our First GAN with PyTorch

5.Generating Images Based on Label Information

6.Image-to-Image Translation and Its Applications

7.Image Restoration with GANs

8.Training GANs to Break Different Models

9.Image Generation from Description Text

10.Sequence Synthesis with GANs

11.Reconstructing 3D Models with GANs

12.Concluding Remarks

13.Appendix

Training StackGAN++ to Generate Images with Better Quality

Steps to train StackGAN++