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Winter Term: 2022 Recent Advances in Generative Models


The last few years have seen tremendous advances in the quality of generated images that become more and more photorealisitic. This semester, we are going to discuss such methods including neural radiance fields and diffusion models. 

Register for course on moodle: https://moodle.uni-siegen.de/course/view.php?id=30323

Qualification Goals

  • Read, understand, and explore scientific literature
  • Summarize a current research topic in a concise report (10 single-column pages + references)
  • Give two presentations about your topic (3 minutes flash presentation, 15 minutes final presentation)
  • Moderate a scientific discussion about the topic of one of your fellow students
  • Review a (draft of a) report of a fellow student


Kickoff-Meeting: October 14th, 1PM
Flash-Presentations: December 9th (tentative)
Final-Presentation: January 31st/ February 7th (tentative)


H-C 6336/37


Winter Term: 2021 Domain Adaptation and Transfer for deep learning models.



Kickoff-Meeting: October 22nd
Flash-Presentations: December 3rd (tentative)
Final-Presentation: January 31st/ February 7th (tentative)


  1. A Fourier-based Framwork for Domain Generalization https://arxiv.org/abs/2105.11120 
  2. FSDR: Frequency Space Domain Randomization for Domain Generalization https://arxiv.org/abs/2103.02370 
  3. Rethinking and Improving the Robustness of Image Style Transfer https://arxiv.org/abs/2104.05623 
  4. Reducing Domain Gap by Reducing Style Bias https://arxiv.org/abs/1910.11645 
  5. What Can Style Transfer and Paintings Do For Model Robustness? https://arxiv.org/abs/2011.14477 
  6. Neural Discrete Representation Learning https://arxiv.org/abs/1711.00937
  7. Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets https://arxiv.org/abs/2107.00860 
  8. Neural Architecture Transfer https://arxiv.org/abs/2005.05859
  9. XferNAS: Transfer Neural Architecture Search [1907.08307] XferNAS: Transfer Neural Architecture Search - arXiv 
  10. Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents https://arxiv.org/pdf/1906.08102.pdf