Machine Learning

      Research Engineer

      Adobe Research

    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.


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Selected Publications

For a full list, have a look at my Google Scholar page.

      CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

      Train a GAN on as few as 10 images of a given character for animation and reposing.

      T. Hinz, M. Fisher, O. Wang, E. Shechtman, S. Wermter, Under Review 2021.

      Improved Techniques for Training Single-Image GANs

      Improving the results and training speed of single-image GANs.

      T. Hinz, M. Fisher, O. Wang, S. Wermter, IEEE Winter Conference on Applications of Computer Vision 2021.

      Semantic Object Accuracy for Generative Text-to-Image Synthesis

      A novel GAN architecture and an improved metric to evaluate generative text-to-image synthesis models.

      T. Hinz, S. Heinrich, S. Wermter, IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.

      Generating Multiple Objects at Spatially Distinct Locations

      Fine-grained control over the placement and identity of objects in images generated with a Generative Adversarial Network.

      T. Hinz, S. Heinrich, S. Wermter, International Conference on Learning Representations 2019.

      Speeding Up the Hyperparameter Optimization Of Deep Convolutional Neural Networks

      How to use lower dimensional data representations to speed up the hyperparameter optimization for CNNs processing images..

      T. Hinz, N. Navarro-Guerrero, S. Magg, S. Wermter, International Journal of Computational Intelligence and Applications 2018.

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Curriculum Vitae