Anime Faces with GANs

# Introduction to Generative Modeling

Deep neural networks are used mainly for supervised learning: classification or regression. Generative Adversarial Networks or GANs, however, use neural networks for a very different purpose: Generative modeling

> Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. - [Source](https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/)


While there are many approaches used for generative modeling, a Generative Adversarial Network takes the following approach:


There are two neural networks: a *Generator* and a *Discriminator*. The generator generates a "fake" sample given a random vector/matrix, and the discriminator attempts to detect whether a given sample is "real" (picked from the training data) or "fake" (generated by the generator). Training happens in tandem: we train the discriminator for a few epochs, then train the generator for a few epochs, and repeat. This way both the generator and the discriminator get better at doing their jobs. GANs, however, can be notoriously difficult to train and are extremely sensitive to hyperparameters, activation functions and regularization. In this tutorial, we'll train a GAN to generate images of anime characters' faces.

I have used [Anime Face Dataset](https://github.com/Mckinsey666/Anime-Face-Dataset), which consists of over 63,000 cropped anime faces. Note that generative modeling is an unsupervised learning task, so the images do not have any labels.

TechStack:

- PyTorch
- Google Colab

Output Link : https://www.youtube.com/watch?v=Cd8cYWgt1Jc