• 09/2020: Some thoughts on my summer internship at Adobe Research.
  • 09/2020: Our work on evaluating GANs got accepted to TPAMI.
  • 06/2020: I started my research internship with Adobe. Big thanks to Adobe for facilitating internships via home-office.
  • 03/2020: We posted our work on Single-Image GANs with the available code and blog post.
  • 01/2020: I will join Matthew Fisher and Oliver Wang at Adobe in San Francisco this summer for a research internship.
  • 10/2019: We uploaded our latest work on generative text-to-image synthesis. Check out our blog post and implementation.
  • 08/2019: I attended the DLRLSS in Edmonton. Have a look at the new blog post for some highlights.
  • 07/2019: I attended this year's EEML in Bucharest and summarized my experiences in a new blog post.
  • 05/2019: I just returned from this year's ICLR. Check out my blog post and see this post which mentions our paper.
  • 01/2019: I started a new position with the Crossmodal Learning (CML) project (subproject A5) at the University of Hamburg.

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Selected Publications

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

      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, Under Review 2020.

      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.

      Image Generation and Translation with Disentangled Representations

      Controllable image generation and translation with very little supervision.

      T. Hinz, S. Wermter, IEEE International Joint Conference on Neural Networks 2018.

      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