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Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.

GAN.tar.gz

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This course is an introduction to Generative Adversarial Networks (GANs) and a practical step-by-step tutorial on making your own with PyTorch. Through this course, you will learn how to build GANs with industry-standard tools.

In the first section, you will dive into PyTorch and refresh your understanding of neural networks by building a simple image classifier. In the second section of this course, you will explore the idea of adversarial training and build progressively more sophisticated GANs; first by learning a simple 1010 pattern, then monochrome images of handwritten digits, and finally full-color images of faces.

The third, and last section, extends the core GAN concept, applying it to convolutional neural networks, and developing a conditional GAN for generating data of the desired class.

By the end of this course, you will have a solid understanding of how to build GANs for your machine learning projects.

Make Your First GAN Using PyTorch

- Working with **monochrome images** *doesn’t need a change in neural network design*. The 2-dimensional array of pixel values are simply unrolled or reshaped into a 1-dimensional list to be fed to the input layer of the discriminator. How this is done isn’t important, but **consistency** is.

- **Mode collapse** is when a _generator only produces output of one class when several are possible_. Mode collapse is one of the most common challenges for GAN training. The causes, and remedies, are still not well understood and are an active area of research.

- A good starting point for **designing GANs is to mirror the generator and discriminator neural network architectures** with the aim of balancing them so one doesn’t get too far ahead of the other during training.

- Experimental evidence suggests the **quality, and not just the quantity, is key to successful GAN training**.

- Smoothly interpolating between generator **seeds** leads to smoothly interpolated images. Summing seeds seem to correspond to additively combining image features. Images from subtracted seeds don’t follow an intuitive pattern.

- The **theoretical ideal MSE loss** for a perfectly trained GAN is **0.25**. For **BCE loss** it is **ln(2)** or about **0.69**.

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Let's summarize how we've used GAN to generate handwritten digits

📚Learning Points in Handwritten Digits

✨Introduction

✨PyTorch Basics

🖥️ Project: Computation Graph and Gradient Calculation

✨First PyTorch Neural Network

✨Refinements

🖥️ Project: Fashion MNIST Classification Model

✨CUDA Basics

Make Your First GAN: Part 1 - Exam

✨The GAN Idea

✨Simple 1010 Pattern

🖥️ Project: A Simple 01110 Pattern GAN

✨Handwritten Digits

🖥️ Project: Fashion MNIST GAN

✨Human Faces

Make Your First GAN: Part 2 - Exam

✨Convolutional GANs

🖥️ Project: A Fashion MNIST CNN

✨Conditional GANs

🖥️ Project: Fashion MNIST Conditional GAN

Make Your First GAN: Part 3 - Exam

✨Conclusion

✨Appendix

📚Learning Points in Handwritten Digits