Presenting a Deep Convolutional Generative Adversarial Network (DCGAN) for generating anime faces. The process involves training a discriminator and generator neural network on a dataset comprising 21,551 manually resized anime faces (64x64 pixels). The generator is capable of producing realistic anime faces after being trained for 50 epochs.

This repository contains notebooks showcasing various generative models, including DCGAN and VAE for anime face generation, an Autoencoder for converting photos to sketches, a captioning model using an attention mechanism for an image caption generator, and more.

Accompanying GitHub repository for the paper "Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures". The paper can be accessed under the following DOI:

A Conditional Deep Convolutional Generative Adversarial Network implemented in PyTorch, trained on the Fashion MNIST dataset.

Using DCGANs to generate anime faces.

Deep Convolutional GAN implementation using pytorch on MNIST dataset.

The CelebA dataset was used to train a DCGAN (Deep Convolutional Generative Adversarial Network). The project was broken down into a series of tasks, from loading in data to defining and training adversarial networks. The trained network generated new images of faces that seemed to be fairly realistic with reduced noise