Generative Adversarial Networks (GANs) consist of two competing neural networks, a generator and a discriminator, working against each other to produce realistic data. Training involves alternating between training the discriminator to distinguish real from fake data and training the generator to create data that can fool the discriminator. This document outlines the fundamental concepts of GANs, including their training process and the use of variations such as Variational Autoencoders (VAEs).

1. GENERATIVE ADVERSARIAL
NETWORKS
11785- Introduction to Deep Learning
AKSHAT GUPTA
KUSHAL SAHARAN

2. –Yann LeCun
“This (GANS), and the variations that are now being
proposed is the most interesting idea in the last 10
years in ML, in my opinion”

3. Video: https://www.youtube.com/watch?v=QiiSAvKJIHo

4. WHAT ARE GANS?
Generative Adversarial Networks

5. WHAT ARE GANS?
Generative Adversarial Networks
Generative Models
We try to learn the underlying the distribution
from which our dataset comes from.
Eg:Variational AutoEncoders (VAE)

6. WHAT ARE GANS?
Generative Adversarial Networks
Generative Models
We try to learn the underlying the distribution
from which our dataset comes from.
Eg:Variational AutoEncoders (VAE)
Encoder
x ~ (training set) P(z) Decoder

7. WHAT ARE GANS?
Generative Adversarial Networks
Generative Models
We try to learn the underlying the distribution
from which our dataset comes from.
Eg:Variational AutoEncoders (VAE)
AdversarialTraining
GANS are made up of two competing networks (adversaries)
that are trying beat each other.

8. WHAT ARE GANS?
Generative Adversarial Networks
Generative Models
We try to learn the underlying the distribution
from which our dataset comes from.
Eg:Variational AutoEncoders (VAE)
AdversarialTraining
GANS are made up of two competing networks (adversaries)
that are trying beat each other.
Neural Networks

9. WHAT ARE GANS?
P(z) Generator
Generated
Data

10. WHAT ARE GANS?
P(z) Generator
Generated
Data
Discriminator Real/Fake?

11. WHAT ARE GANS?
P(z) Generator
Generated
Data
Discriminator Real/Fake?
Real
Data

12. HOWTOTRAIN A GAN?

13. HOWTOTRAIN A GAN?
At t = 0,
Generator
Latent
Vector
Generated
Image
(fake image)
Generated
Data
Discriminator Real/Fake?
Given
Training
Data
(fake data)
(Real data)

14. HOWTOTRAIN A GAN?
At t = 0,
Generator
Latent
Vector
Generated
Image
(fake image)
Generated
Data
Discriminator Real/Fake?
Given
Training
Data
(fake data)
(Real data)
Binary
Classifier

15. HOWTOTRAIN A GAN?
Which network should I train first?

16. HOWTOTRAIN A GAN?
Which network should I train first?
Discriminator!

17. HOWTOTRAIN A GAN?
Which network should I train first?
Discriminator!
But with what training data?

18. HOWTOTRAIN A GAN?
Which network should I train first?
Discriminator!
But with what training data?
The Discriminator is a Binary classifier.
The Discriminator has two class - Real and Fake.
The data for Real class if already given:THETRAINING DATA
The data for Fake class? -> generate from the Generator

19. HOWTOTRAIN A GAN?
What’s next? ->Train the Generator
But how? What’s our training objective?

20. HOWTOTRAIN A GAN?
What’s next? ->Train the Generator
But how? What’s our training objective?
Generate images from the Generator
such that they are classified incorrectly by the Discriminator!

21. HOWTOTRAIN A GAN?
Discriminator
Step 1:
Train the Discriminator
using the current ability
of the Generator.

22. HOWTOTRAIN A GAN?
Generator
Discriminator
Step 1:
Train the Discriminator
using the current ability
of the Generator.
Step 2:
Train the Generator
to beat
the Discriminator.

23. HOWTOTRAIN A GAN?
Generator
Discriminator
Step 1:
Train the Discriminator
using the current ability
of the Generator.
Step 2:
Train the Generator
to beat
the Discriminator.
Generate images from the Generator
such that they are classified incorrectly by the Discriminator!

24. HOWTOTRAIN A GAN?
Generator
Discriminator
Step 1:
Train the Discriminator
using the current ability
of the Generator.
Step 2:
Train the Generator
to beat
the Discriminator.

25. MNIST AND FASHION-MNIST