Generative adversarial networks (GANs) are a class of machine learning frameworks where two neural networks compete against each other. A generator network produces synthetic data to fool a discriminator network, while the discriminator learns to assess whether samples are real or generated. This adversarial process allows GANs to learn the true data distribution and generate highly realistic synthetic examples. The document provides background on machine learning, artificial intelligence, computer vision, and GANs. It explains how GANs work mathematically through a minimax game and value function, and shows how the generator and discriminator improve through training. The document discusses advantages like sharp representations, discusses challenges like training instability, and provides examples of GAN applications.

1. Generative
Adversarial Nets
(1406.2661)
Sajal Rastogi

2. Background
Machine Learning
Machine learning (ML) is the study of computer
algorithms that improve automatically through
experience. It is seen as a subset of artificial
intelligence. Machine learning algorithms build a
model based on sample data, known as "training
data", in order to make predictions or decisions
without being explicitly programmed to do so.
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Artificial Intelligence
Artificial intelligence (AI) is wide-ranging
branch of computer science concerned with
building smart machines capable of
performing tasks that typically require
human intelligence

3. Background
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4. Background
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Computer Vision
It is defined as a field of study that seeks to develop techniques to help computers “see” and understand the
content of digital images such as photographs and videos.Computer vision tasks include methods for acquiring,
processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real
world in order to produce numerical or symbolic information, e.g. in the forms of decisions
At an abstract level, the goal of computer vision problems is to use the observed image data to infer something about the
world.
Various applications are - HealthCare , Augmented Reality , Facial Recognition , Self-Driving Cars.

5. Background
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Supervised Learning
Supervised learning is where you have input variables (x) and
an output variable (Y) and you use an algorithm to learn the
mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that
when you have new input data (x) that you can predict the
output variables (Y) for that data.
Examples → Classification and Regression
Unsupervised Learning
Unsupervised learning is where you only have input data (X)
and no corresponding output variables.
The goal for unsupervised learning is to model the underlying
structure or distribution in the data in order to learn more about
the data.
These algorithms tend to have a larger complexity. And less
accuracy
Example → Clustering , Association

6. GANS !
Paper by - Ian J. Goodfellow, Jean Pouget-Abadie , Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair† , Aaron Courville, Yoshua Bengio 6

7. What is GAN’s ??
Generative
A neural network that takes as
input random noise and
transforms it into a sample
from the model distribution
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Adversarial
A conflict based system for better
understanding of data distribution in form
of discriminator.
Nets
Neural Network structure

8. Why GAN’s ??
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According to paper there were many machines which have used generative models and other
methods like approximating maximum log likelihood method , Graphical methods but came out
to be less successful in terms of accuracy and realism.
They were unable to extract the probability distribution of the dataset for intractable problems.
The discovery of adversarial nets made it possible.
They work side by side to compete with each other and get maximum possible probability
distribution possible.

9. How does it Work ?
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10. How does it Work ?
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11. How does it Work ?
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12. How does it Work ?
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13. How does it Work ?
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14. How does it Work ?
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15. Maths ? (Value Function)
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Generator’s distribution pg over data x, we define a prior on input noise variables pz(z)
G is a differentiable function of multilayer perceptron
D(x) represents the probability that x came from the data
We train D to maximize the probability of assigning the correct label to both training
examples and samples from G. We simultaneously train G to minimize log(1 − D(G(z)))
Ex : expectation of x which can be understood as summation/integral
Cost_function(generator) = - Cost_function(discriminator)

16. Maths ? (Value Function contd. )
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Two-player minimax game with value function V (G, D)
Optimization is achieved by minimizing and maximizing the entropy loss alternatively for two i.e. generator and
discriminator.
This function moreover looks like binary cross entropy loss function

17. Maths ? (Value Function contd. )
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Everything to the left of the boundary is 1 and to the right is 0

18. Maths ? (Value Function contd. )
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A better trained model have a boundary such that all ones are above the
boundary and 0 Below

19. Maths ? (Value Function contd. )
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We have ignored the first term while minimizing the generator because it
does not contain any generator term .
So we will just consider second term while minimizing the generator part.

20. More on Maths
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21. More on Maths (Minima)
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KL divergence is used to prove how much two distributions are similar

22. How does maths work
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Green line -- Model Distribution
Blue dashed line -- Discriminators Response
Black dotted line -- Data Distribution
As the training proceeds results starts to get better and better.

23. How does maths work
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a) Only random noise is send initially and architecture is performing poorly.
b) Updating D which start to converge at maxima
c) Updating G
d) Mixed strategy Equilibrium (pg = pdata)

24. Algorithm
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25. Results
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A) MNIST
B) TFD
C) CIFAR-10

26. Comparison
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27. Advantages And Disadvantages
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Advantages →
● No complex technique is needed to obtain the gradients
● They can represent very sharp, even degenerate distributions
● GANs generate data that looks similar to original data
● GANs go into details of data and can easily interpret into different versions so it is helpful in doing machine
learning work.
Disadvantages →
● G must be well synchronized with D during the training process
● No explicit representation for data distribution
● You need to provide different types of data continuously to check if it works accurately or not.
● Hard to train

28. Conclusions
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1. Sampling is done using a neural network.
2. Learned approximate inference can be performed by training an auxiliary network to predict z given x. This is similar
to the inference net trained by various other algorithm but with the advantage that the inference net may be trained for
a fixed generator net after the generator net has finished training.
3. Essentially, one can use adversarial nets to implement a stochastic extension of the deterministic.
4. Semi-supervised learning: features from the discriminator or inference net could improve performance of classifiers
when limited labeled data is available.
5. Efficiency improvements: training could be accelerated greatly by divising better methods for
coordinating G and D or determining better distributions to sample z from during training.

29. Real World Applications and other researches
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Toonify
Text to image

30. Real World Applications and other researches
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Super Resolution
Next Frame Prediction Video
Clothing translation
3-D object generation
Photo to Emoji
Drawing to image
Auto Photo Editing

31. The Most Dangerous Application right now
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32. 32
THANK
You!

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References →
https://arxiv.org/abs/1406.2661
https://www.youtube.com/watch?v=MKedB9qOHi4&t=11s
https://www.youtube.com/watch?v=Gib_kiXgnvA
Contact Info →
https://www.linkedin.com/in/rsajal/
https://github.com/r-sajal
sajalrastogi03@gmail.com