A quick presentation of GAN made on canva

1. The Future of GANs:
Innovations and
Emerging Trends
A QUICK PRESENTATION ON:
A Comprehensive Dive into GANs' Innovations
and Emerging Trends
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2. Members
Rasik Dhakal Omraj Budhathoki
Sugat Shakya

3. 1
2
Content
OPERATIONAL
METHODOLOGY
INTRODUCTION APPLICATIONS
& EMERGING
TRENDS
INNOVATIONS
FUTURE
POTENTIAL
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4. Generative adversarial
networks (GAN’s)
Introduction:
GAN was Developed by Ian Goodfellow and team in 2014.
GAN is a class of machine learning frameworks.
Key for generative AI.
Components:
Two neural networks: Generator and Discriminator.
Trained simultaneously.
Competitive Training:
Generator creates data.
Discriminator distinguishes real from generated.
Continuous improvement through competition.
Objective:
Enhance and achieve desired results.
Revolutionized generative models.

5. Generator Discriminator
This neural network generates new
data instances, such as images,
based on random input.
Job: Creates fake data (like images)
from random noise.
Example: Forgers making
counterfeit paintings.
This neural network evaluates the
generated data, along with real
data.
Job: Judges if data is real or fake.
Example: Art critic checking if a
painting is genuine.
Generative Adversarial Networks (GANs) work like a creative duo:
a "Generator" and a "Discriminator."

6. HOW IT WORKS?
Process:
Generator creates fake data.
Discriminator reviews and tries to distinguish real from fake.
They both improve over time through competition.
Result: Generator gets skilled at creating realistic data.
Outcome:
GANs generate high-quality, realistic data, like images or even
text, by learning from each other in a back-and-forth process.
THEY FOLLOW ZERO SUM RULE

8. G D

9. Image Editing and Generation
GANS’s can fill in missing parts of photos, enhance resolution, or
even create entirely new images that mimic any style you desire.
Medical Imaging and Diagnosis
By generating synthetic medical images, from MRIs to X-rays,
GANs can help doctors train AI algorithms for more accurate
diagnoses, especially in cases where real data is scarce.
Fashion and Design
GANs are revolutionizing the fashion industry by generating
innovative garment designs, predicting upcoming trends, and
even personalizing outfits based on individual preferences.
Drug Discovery and Materials Science
GANs can now accelerate the discovery of new drugs and
materials by generating novel molecular structures with desired
properties.
Application of
GAN’s
HOW GAN’S HELPS IN
MACHINE LEARNING ?

10. Innovations
of GANs
The future of Generative
Adversarial Networks (GANs) holds
exciting potential for innovation
and impact across multiple
domains.
Artificial Intelligence and Creativity:
GANs advance AI capabilities for creating lifelike
images, artwork, music, and literature
They produce diverse and realistic synthetic data,
pushing the boundaries of AI creativity
This potential fosters the emergence of new artistic
expression and innovative tools
Data Augmentation and Synthetic Data
Generation:
GANs transform data utilization in enhancing
machine learning tasks, especially through
augmentation
By generating realistic synthetic data, GANs
address limited data availability
They enhance the effectiveness and flexibility of
machine learning models in healthcare, finance, and
autonomous systems

11. Innovations
of GANs
The future of Generative
Adversarial Networks (GANs) holds
exciting potential for innovation
and impact across multiple
domains.
Enhanced Visual Effects and Virtual
Environments:
GANs contribute to more realistic visual effects in
entertainment
They generate high-fidelity imagery and
environments
GANs elevate the level of immersion and realism in
movies, video games, and virtual reality
Ethical and Security Implications:
GANs raise ethical concerns and possible misuse
Concerns include the creation of convincing fake
content, deepfakes, and privacy issues
Progress in GANs requires simultaneous progress
in ethical standards and security measures

12. Revolutionize approaches to creativity, research, and
development
Students have more freedom to choose the methods and tools
that help them learn best.
Improves Training Stability
Scientists are now training the stability of GANs to increase
realizability and ability of GANs to produce high quality images
Implemented in Self supervised learning
Future of GANs have the possibility and potential to open up new
doors for learning unlabeled data and enhanging model
performance.
Future
Potential of
GAN’s

13. WHO IS THIS PERSON ?

14. "GANs are like two
mischievous kids playing a
game of one-upmanship,
constantly pushing each other
to become better artists. And
in the process, they're creating
some truly stunning works of
art."
-BARD

15. IN CONCLUSION, THE FUTURE OF GANS INNOVATION IS LIKELY TO BE MARKED BY
ADVANCEMENTS IN CREATIVE AI, DATA GENERATION AND AUGMENTATION,
PERSONALIZED CONTENT CREATION, AND TRANSFORMATIVE APPLICATIONS
ACROSS DIVERSE INDUSTRIES. AS GANS CONTINUES TO EVOLVE, THEIR IMPACT
ON TECHNOLOGY, CREATIVITY, AND SOCIETY IS EXPECTED TO BE SUBSTANTIAL.
CONCLUSION
MOST OF THE PICTURES ARE TAKEN FROM GOOGLE IMAGES

16. Do you have
any questions?
Send it to us! We hope you
learned something new.