I am a machine learning reseach engineer at Adobe Research and a 4th-year PhD student at the Knowledge Technology group at the University of Hamburg. I work on generative models that learn visual representation in a semi- or unsupervised way. This includes applications such as unconditional image generation, text-to-image synthesis, and disentangled representations. I am also interested in few-shot learning with and the automatic evaluation of generative models.
I completed my Bachelor degree in Business Informatics at the University of Mannheim, during which I also studied at the National University of Singapore for one semester. Following that, I did a research oriented Master degree in Intelligent Adaptive Systems at the University of Hamburg.
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.