Generative Adversarial Network (GAN) extensions and applications in image processing and generation When human brains process input from the senses they are able to effortlessly imagine new instances and scenarios from only a small amount of input experience. Generative Adversarial Networks (GANs) manages to some extent to achieve this imagination ability for datasets. We explore this technique and its applicability in image processing and generation. This has applications in areas like medicine, physics, and artificial intelligence.

Project thesis Currently I’m writing my master thesis on Generative Adversarial Networks. This is a exciting technology that has been pushing the boundaries of AI and of what is conceived possible. I started to write my projet thesis on several techniques and theortical insights involving neural networks. It can be seen here. I will update this with my master thesis later.

Who am I now My interest for mathematical research stems from my curiosity of understanding the systems and structures that make up the world as well as the intellectual satisfaction of understanding the high abstractions of mathematics. However I do believe that the most prosperous discoveries and innovations of mathematics are those that connect deep insights and applicability. This is why I want to have a focus on applications of mathematics; or more specifically I want to further investigate machine learning, artificial intelligence and deep learning and the opportunities it unveils.