Generative Adversarial Networks (GANs) Explained: How They Work, Types, and Applications
In recent years, Generative Adversarial Networks (GANs) have become one of the most exciting advancements in artificial intelligence. Capable of creating highly realistic images, synthesizing lifelike voices, and even generating entirely new music compositions, GANs are revolutionizing creative workflows and synthetic data generation.
In this video, we'll explore:
-- What exactly is a GAN?
-- How do GANs work? The basics of adversarial training.
-- A step-by-step look at GAN training.
-- Different types of GANs, such as Conditional GANs, DCGANs, WGANs, and CycleGANs.
-- Exciting applications of GANs, from image synthesis and text-to-image generation to healthcare and data augmentation.
-- Challenges in training GANs, like instability, mode collapse, and vanishing gradients.
The video with Pytorch coding for a simple GAN application is here:
https://youtu.be/KQM6nuNsyhA
Thank you!
Dr. Shahriar Hossain
https://computing4all.com
In recent years, Generative Adversarial Networks (GANs) have become one of the most exciting advancements in artificial intelligence. Capable of creating highly realistic images, synthesizing lifelike voices, and even generating entirely new music compositions, GANs are revolutionizing creative workflows and synthetic data generation.
In this video, we’ll explore:
— What exactly is a GAN?
— How do GANs work? The basics of adversarial training.
— A step-by-step look at GAN training.
— Different types of GANs, such as Conditional GANs, DCGANs, WGANs, and CycleGANs.
— Exciting applications of GANs, from image synthesis and text-to-image generation to healthcare and data augmentation.
— Challenges in training GANs, like instability, mode collapse, and vanishing gradients.
The video with Pytorch coding for a simple GAN application is here:
https://youtu.be/KQM6nuNsyhA
Thank you!
Dr. Shahriar Hossain
https://computing4all.com
