As part of a university module, a peer and I trained and tested Generative
Adversarial Networks (GAN) to generate synthetic handwritten digit images,
utilising the MINST dataset.
We designed various DCGAN's and CGAN's to use as Generator and Discriminator
models, then evaluated their performance.
We discussed our findings in
a report.
All relevant code can be found in
the associated GitHub repository.