Generative advesarial networks technical seminar

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