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Current issues, methods and datasets in computer vision for image classification, object detection, image segmentation, image generation and domain transfer and semi-supervised learning. All lecture material such as slides, recordings, as well as the exercises are available in the lecture moodle.

Programming exercises will be implemented in python and pytorch.



  • regular Lecture: Monday:  4-6pm, starting on 10.10.2022
  • Exercise: Monday, 6-8pm, Start: 10.10.2022
  • Both lecture and exercise will also be streamed online via zoom. Please make use of this option in case you feel sick. 

Qualification Goals

  • Deep understanding of current computer vision methods for image classification, object detection, image segmentation, image generation, and domain transfer.
  • Understand, apply and evaluate current approaches.
  • Understanding of the technical principles of computer vision methods.
  • Evaluation and discussion of new computer vision problems and methods.


  • R. Szeliski: Computer Vision Algorithms and Applications, Springer, 2010. ISBN: 978-1-84882-934-3. (Online available: http://szeliski.org/Book/).

  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, An MIT press book, 2016