This is a short yet informative presentation on one of the most popular machine learning model Generative Adversarial Network (GAN).

1. Date: 14 Dec,2023
Riwaz Mahat
Ashim Neupane
Prasis Gautam

2. Challenges and
Future of GANs
Types of GANs and
Use Cases
Architecture of
GANs
• Brief overview of GANs
• How GANSs work
• Key concepts
⚬ Generator
⚬ Discriminator
• Overview of different
types of GANs
• Real-world use cases
of GANs
• Detailed look at the
architecture of GANs
• Discussion of challenges
in training GANs
• Future trends and
research
TABLE OF CONTENTS
Understanding the
GANs

3. WHAT EXACTLY IS GAN ?
GAN, Generative Adversarial Network is a type of
machine learning model comprising two neural
networks: Generator and Discriminator, competing
against each other to generate realistic data, enabling
the creation of high quality synthetic content such as
images, videos, and text.
GANs leverage a game-theoretic framework
where the generator learns to produce
increasingly convincing data while the
discriminator aims to distinguish between real
and generated samples, fostering the
generation of diverse and realistic outputs.
HOW DOES IT WORK ?

4. UNDERSTANDING GAN
KEY CONCEPTS
GENERATOR
DISCRIMINATOR
• Generator: Creates synthetic data resembling the real dataset from
random noise.
• Discriminator: Distinguishes between real and synthetic data,
improving its accuracy.
• Adversarial Training: Simultaneous training of generator and
discriminator in a competitive manner.
• Loss Function: Guides training by measuring network performance.
• Generator: produces synthetic data from noise input.
• Discriminator: Distinguishes between real and synthetic data.
• Adversarial Process: Generator deceives discriminator and it distinguishes better.
• Iterative: Both networks improve until generator creates highly realistic data.
• Outcome: High-quality synthetic data creation.
WORKING

5. Neural network layers which generates
realistic data to deceive the discriminator
GENERATOR
Neural network layers for distinguishing real
from generated data which enhances
accuracy in discriminating real and fake data
DISCRIMINATOR
ARCHITECTURE
OF
GAN
It follows simultaneous training where
generator improves to create more
convincing data and discriminator enhances
discrimination abilities
TRAINING PROCESS
GANs evolve through adversarial training to
produce high-quality, realistic synthetic data
resembling the original dataset
OUTCOME

6. TYPES OF GAN
• Vanilla GAN: This is the simplest type of GAN, composed of a generator
and a discriminator.The generator captures the data distribution, while
the discriminator tries to determine the probability of the input.
• Conditional GAN (CGAN): Here, both the generator and discriminator are
provided with additional information, such as a class label or any modal
data. This extra information assists the discriminator in determining the
conditional probability instead of the joint probability.
• Deep Convolutional GAN (DCGAN): This is the first GAN where the
generator used a deep convolutional network, resulting in the generation
of high-resolution and quality images.
• CycleGAN: This GAN is designed for Image-to-Image translations, meaning
one image is mapped to another image. For instance, it can convert
summer images into winter images and vice versa by adding or removing
features.
• Generative Adversarial Text to Image Synthesis: This type of GAN is used
to generate images from text descriptions.

7. REAL WORLD USE
CASES
GANs can generate new, realistic images that are
similar but specifically different from a dataset of
existing photographs. This can be used for tasks
like creating new designs, generating artwork, or
producing realistic video game graphics.
IMAGE SYNTHESIS
01
GANs can convert one type of image into
another. For example, CycleGAN can convert
summer images into winter images and vice
versa.
IMAGE-TO-IMAGE TRANSLATION
02
GANs can generate images from text descriptions.
This can be used in a variety of applications, such
as creating visual content from written
descriptions or aiding in the design process.
Text-to-Image SyNTHESIS
03

9. CHALLENGES
Hindered training due to
gradient issues.
VANISHING GRADIENTS
Lack of standardized metrics for
GAN assessment.
EVALUATION
METRICS
High sensitivity to
hyperparameter values.
HYPERPARAMETER
SENSITIVITY
Limited variety of generated outputs
and techniques.
MODE COLLAPSE
Convergence difficulties between
generator and discriminator.
TRAINING INSTABLITY

10. FUTURE TRENDS AND
RESEARCH OF GAN
• Improved Stability and Training Techniques
• Diversity and Realism Enhancement
• Interdisciplinary Applications
• Ethical Considerations and Regulations
• Hardware & Software Advancements
• Adversarial Learning Beyond GANs

11. CONCLUSION
ANY
QUESTIONS ?
• In simple terms, Generative Adversarial Networks
(GANs) are a cool technology in artificial intelligence.
• They use two parts, a generator and a discriminator,
to create realistic fake data.
GANs have been awesome for making lifelike
medias like photos, vidoes, graphics and more.
• They're like a creative duo where one tries to make
things look real, and the other tries to figure out if
they're fake.
• Despite their success, challenges such as training
stability, mode collapse, and ethical considerations
remain areas of ongoing research.
• Overall, GANs have opened up exciting possibilities
in AI, making things like generating realistic content a
lot more fun and interesting.