Generation of Real Looking Images Using GANs

Automatic image generation is a complex task with many applications in several domains such as security (e.g., gener- ating portraits from the description), styling and entertainment. In this project, advanced versions of Generative Adversarial Networks (GANs) are used to generate real images. A conditional GAN (cGAN) was implemented, followed by an evaluation with visual examination, k-nearest-Neighbours (kNN) and Fre ́chet Inception Distance (FID). The evaluation indicates that a cGAN can generate realistic images of handwritten digits. Whereas, the discussions shows that more work must be done to create realistic images from datasets which contains larger and more complex images.

• Code as iPython
• Project introduction as PDF
• Project presentation as PDF
• Project report as PDF

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conda env create -f environment.yml
conda activate mad40-env

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Just pull the repo, if you wanna change sth you can ask :)

Please do NOT commit any data files into the repositories. Data should always be kept seperate from code!

• Timo Bohnstedt - Coding, Report - GitHub Bohniti

Pretty much the BSD license, just don't repackage it and call it your own please! Also if you do make some changes, feel free to make a pull request and help make things more awesome!

The author would like to thank his Franz Koeferl and the MADI40-Team from the Machine Learning & Data Analytics Lab for excellent support.