This repository contains my solutions to a 3rd year coursework assignment on Deep Learning at Durham University.
This coursework required the implementation of a Deep Learning model to synthesize images based on the CIFAR-10 and STL-10 datasets. While any method could be used, I opted to implemented a Wasserstein GAN which utilised Gradient Penalty and Auxiliary Classification as described here. The implentation was developed using PyTorch and can be found in the file ac-wgan-gp.py. The results of the model can be seen in the report included in this repository.
The final mark received was 84%.
By boyla950.