A Gentle Introduction to BigGAN the Big Generative Adversarial Network

Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis.

Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results.

The BigGAN is an approach to pull together a suite of recent best practices in training class-conditional images and scaling up the batch size and number of model parameters. The result is the routine generation of both high-resolution (large) and high-quality (high-fidelity) images.

In this post, you will discover the BigGAN model for scaling up class-conditional image synthesis.

After reading this post, you will know:

• Image size and training brittleness remain large problems for GANs.
• Scaling up model size and batch size can result in dramatically larger and higher-quality images.
• Specific model architectural and training configurations required to scale up GANs.

Kick-start your project with my new book Generative Adversarial Networks with Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.