TOBIAS HINZ
PhD Candidate
University of Hamburg
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.
News
- 01/2021: I started a new position as machine learning research engineer with Adobe Research.
- 11/2020: Our work on Single-Image GANs (code available) got accepted to WACV 2021.
- 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.
My Remote Internship with Adobe Research (Summer 2020)
Posted on 18 Sep 2020 | Reading time: 3 minutes
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.
Improved Techniques for Training Single-Image GANs
Posted on 24 Mar 2020 | Reading time: 7 minutes
Overview of our paper about training GANs on a single image for tasks such as image generation, image harmonization, and image animation.
Semantic Object Accuracy for Generative Text-to-Image Synthesis
Posted on 30 Oct 2019 | Reading time: 8 minutes
Overview of our paper about our new model and evaluation metric for generative text-to-image synthesis models.
My Time At The Deep Learning And Reinforcement Learning Summer School (DLRLSS) in Edmonton (Canada)
Posted on 06 Aug 2019 | Reading time: 17 minutes
After attending the EEMLSS I was lucky enough to also attend the Deep Learning And Reinforcement Learning Summer School (DLRLSS) in Edmonton (Canada) 2019.
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Selected Publications
For a full list, have a look at my Google Scholar page.
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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