kaggle_username

kaggle_key

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

torch

J03T

J03O

J03P

J04AF

J04OF

J04N

J04CR

J05UCP

J07TMG

J07TTMG

J16TG

J16CGR

JAppebdixB

JAppebdixD

SPA04

SPA16

SPA13

SPA09

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

# What is mode collapse?

What we’ve just seen in the previous lesson is unfortunately a very **common problem with GAN training** called mode collapse.

In our MNIST example, we want the generator to be able to create images of all ten digits. When **mode collapse happens, the generator will only produce one, or a small number, of the possible ten digits.** We can see our own generator suffered from mode collapse because it created the same image again and again.

# Why does the mode collapse?

The reasons why mode collapse happens are not perfectly understood. Research is ongoing and active, and some ideas are more established than others.

One way of looking at mode collapse is to see that the generator has raced ahead of the discriminator and found a single image that always gets classified as real, before the discriminator has learned to provide good quality feedback to the generator. To remedy this, it is tempting to try ideas like training the discriminator more often than the generator. In practice, this has been found not to work well. This suggests the solution lies not just in the quantity, but in the quality of training. 

In our example, the rising loss values from the generator show that it is not learning, perhaps because the discriminator is not doing a good job providing it with good feedback. Again, this points to training quality as the challenge.



Learn about mode collapse, what it is, why it happens and how we can get rid of it.

Mode Collapse

✨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

Mode Collapse

What is mode collapse?