What if a machine were able to paint, compose a piece of music, or generate a photo of a human that looks genuinely authentic? That’s what Generative Adversarial Networks (GANs) offer—a breakthrough in deep learning where two neural networks are set against one another in a creative duel.
In this talk, I will walk through GANs in an easy, story-based manner, explaining how a “generator” and a “discriminator” play the game to build something new and realistic. From art and design to healthcare and security, GANs are transforming the way we create and imagine with AI.
We will journey into both the theory of GANs and their practical applications, while also addressing challenges like training instability and ethical concerns. By the end, attendees will see GANs not as an intimidating black box, but as an exciting tool spearheading the next generation of artificial intelligence.
What we’ll cover:
Demystifying GANs: how the generator–discriminator game works
Popular variants of GANs (DCGAN, CycleGAN, StyleGAN) and their importance
Real-world applications: art, entertainment, data augmentation, and healthcare
Key challenges: mode collapse, evaluation, and ethical issues
Beginner-friendly tools and platforms to try GANs hands-on
Attendees will leave with:
A clear understanding of how GANs function
Knowledge of popular GAN variants and their use cases
Insights into the biggest real-world applications of GANs
Awareness of ethical issues and technical challenges in GAN training
Resources and beginner-friendly tools to start experimenting with GANs