This repository contains code and resources for a 7-day series on creating Generative Adversarial Networks (GANs) from scratch. Each day's code focuses on a specific aspect of GANs and is designed to help you build a strong understanding of the underlying concepts.
Fully Convolutional GANs (FC-GANs) extend the principles of convolutional neural networks (CNNs) to Generative Adversarial Networks (GANs). Unlike traditional GANs that might use a combination of fully connected and convolutional layers, FC-GANs rely entirely on convolutional layers for both the generator and discriminator.
Deep Convolutional Generative Adversarial Networks (DCGANs) are a variant of GANs designed specifically for image generation tasks. DCGANs utilize convolutional layers in both the generator and discriminator, making them highly effective in capturing spatial hierarchies and patterns in image data.
Wasserstein Generative Adversarial Networks (W-GANs) represent a variant of traditional GANs designed to overcome challenges such as mode collapse and training instability. The key innovation lies in using the Wasserstein distance as the metric for training, providing a more meaningful and stable objective compared to the original GAN formulation.
Conditional Generative Adversarial Networks open up a world of possibilities in the realm of generative models. Whether you're interested in creating art, enhancing datasets, or translating between domains, CGANs provide a powerful framework for controlled and targeted data generation.