I am a machine learning reseach engineer at Adobe Research working on GANs for image generation and editing. I am interested in using generative models to learn image representations that allow for both editing and faithful reconstruction of the original input, e.g. faces or bodies. Other reseach interests of mine include unconditional image generation, text-to-image synthesis, and compositional representations for complex scenes. I am also interested in few-shot learning and the automatic evaluation of generative models.
I obtained my PhD at the Knowledge Technology group at the University of Hamburg (Germany). Before that, I completed a research oriented Master's degree in Intelligent Adaptive Systems at the University of Hamburg. I received a Bachelor's degree in Business Informatics at the University of Mannheim, during which I also studied at the National University of Singapore for one semester.
I had the chance to do an internship with Adobe Research in San-Francisco. Due to COVID all Adobe staff are working from home and so I did my internship remotely from Germany.
Overview of our paper about training GANs on a single image for tasks such as image generation, image harmonization, and image animation.
Overview of our paper about our new model and evaluation metric for generative text-to-image synthesis models.
After attending the EEMLSS I was lucky enough to also attend the Deep Learning And Reinforcement Learning Summer School (DLRLSS) in Edmonton (Canada) 2019.
For a full list, have a look at my Google Scholar page.
T. Hinz, N. Navarro-Guerrero, S. Magg, S. Wermter, International Journal of Computational Intelligence and Applications 2018.