Understanding Generative Adversarial Networks (GANs) – A Deep Dive

Generative Adversarial Networks (GANs) are a powerful framework for training generative models. They consist of two components – a generator and a discriminator, which work together in a competitive process to improve the quality of generated samples. The generator takes random noise as input and generates an image, while the discriminator tries to distinguish between real and fake samples.

The competition between the generator and discriminator is framed as a minimax game, where the generator aims to maximize the discriminator’s loss on its generated samples, while the discriminator aims to minimize its misclassification loss. This leads to a dynamic training process where both components are continuously improving.

The ultimate goal of GANs is to generate realistic samples that are indistinguishable from real data. This can have wide-ranging applications in image generation, video synthesis, and more. By leveraging the power of deep learning and adversarial training, GANs have pushed the boundaries of what is possible in generative modeling.

In future blog posts, we will delve deeper into the inner workings of GANs, explore different variations of the framework, and discuss practical applications in various domains. Stay tuned for more insights and updates on this exciting field of research!

References:
– https://skymind.ai/wiki/generative-adversarial-network-gan
– https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09