A ppt on a technical seminar on generative advesarial networks

1. Bharath Gowda R - 1MV20IS015
Generative Adversarial
Networks
Technical Seminar

2. What is GAN?
GAN stands for Generative Adversarial Network. It's a type of artificial intelligence (AI) framework that uses
two neural networks to compete against each other.
There are two main components in a GAN system:
◦Generator: This network creates new data, like images or text, based on the training data it receives.
Imagine it as an artist who is trying to imitate a particular style.
◦Discriminator: This network acts like a critic, trying to determine if the new data generated by the first
network is real or fake. It compares the generated data with the original data to make this judgement.

3. How does it work?
Adversarial Training: The two neural networks are pitted against each other in
a competition. The generator keeps trying to improve its forgeries to fool the
discriminator, while the discriminator gets better at spotting the fakes. This
competition helps both networks learn and improve over time.
New Data Creation: As the generator gets better at creating new data that
deceives the discriminator, it essentially learns the underlying patterns and
features of the real data. This allows it to generate entirely new data samples
that are similar to the training data.

4. FLOW CHART
ThE process iterates continuously. The generator constantly learns from the
discriminator's feedback, refining its ability to generate realistic data. Meanwhile, the
discriminator is challenged by the generator's evolving outputs, improving its
discrimination capabilities. Over time, this adversarial training loop leads to a
generator capable of producing high-quality, realistic data.

5. Challenges and Solutions:
Mode Collapse: This occurs when the generator gets stuck in a loop, producing
only a limited set of outputs that may not represent the full diversity of the real data.
Solutions: Techniques like spectral normalization and gradient penalty are used to
encourage diversity in the generated data.
Vanishing Gradients: This problem arises when the training signal weakens as it
propagates through the network, hindering the generator's learning process.
Solutions: Techniques like leaky ReLU activations and weight initialization
strategies are employed to address vanishing gradients.

6. Applications of Generative Adversarial Networks
(GANs)
The ability of GANs to generate novel and realistic data unlocks a
vast array of applications across diverse domains. Here, we
explore some of the most exciting and impactful applications of
GANs

7. Image Generation
Stock Photos: GANs can generate high-resolution, royalty-free
images for various uses in advertising, marketing, and design.
Photo Restoration: Damaged or incomplete images can be repaired
by GANs, filling in missing parts and restoring them to their original
glory (photo inpainting).
Creative Content Design: GANs can be used to generate novel
fashion designs, create unique artistic styles based on existing
artwork, or even develop personaliSed avatars.

8. Data Augmentation
In machine learning, having a large and diverse dataset is crucial for
optimal model performance. GANs can be employed to artificially generate
new data instances that share the characteristics of the real data. This data
augmentation technique helps improve the robustness and generalisability
of machine learning models.

9. Medical Applications
Synthetic Medical Images: GANs can generate synthetic medical images like MRIs and
CT scans for training and testing medical imaging algorithms. This reduces the reliance
on real patient data and allows for the creation of more diverse and specific datasets for
medical research.
Surgical Simulation: Realistic simulations of surgeries can be created using GANs,
allowing surgeons to practice complex procedures in a safe and controlled environment.
Fashion Industry
•Clothing Style Transfer: GANs can enable virtual try-on experiences, allowing
users to see how different clothing styles look on them before making a
purchase.
•Personalised Recommendations: GANs can be used to personalise fashion
recommendations based on individual preferences and styles.

10. Gaming Industry
•Game Environments: Realistic and immersive game
environments can be generated using GANs, enhancing
the overall gaming experience.
•Character Design: GANs can be employed to create
unique and diverse character designs, adding richness and
variety to video games.
•Non-Playable Characters (NPCs): More realistic and
engaging NPCs can be developed using GANs, improving
the overall immersion and interactivity within games.

11. CONCLUSION
Looking ahead, the future of GANs is brimming with possibilities. As
research continues to flourish, we can expect even more
sophisticated and versatile GAN architectures to emerge. This will
undoubtedly unlock a new era of innovation, with GANs playing a
pivotal role in shaping the future of artificial intelligence and its
impact on various aspects of our lives. The ability to create entirely
new yet realistic data paves the way for advancements that were
once unimaginable, and GANs stand at the forefront of this exciting
new frontier.

12. THANK YOU