Solving Inverse Problems with Deep Learning (Deep learning and inverse problems)
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
EI71068 is a semester module in English language at which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|180 h||60 h||6 CP|
Content, Learning Outcome and Preconditions
Today, the best performing approaches for the aforementioned image reconstruction and sensing problems are based on deep learning, which learn various elements of the method including i) signal representations, ii) stepsizes and parameters of iterative algorithms, iii) regularizers, and iv) entire inverse functions. Motivated by those success stories, researchers are redesigning traditional imaging and sensing systems, and deep learning based signal reconstruction methods are starting to be used in important imaging technologies, for example in GEs newest computational tomography scanners and in the newest generation of the iPhone.
This course gives a graduate/master level introduction to deep learning based imaging. The course first introduces classical approaches to solving inverse problems and then aims to explain the recent advances of deep neural network based approaches for solving inverse problems in the imaging sciences.
Topics include classical sparse models, optimization for fitting classical methods and for training deep networks, unrolled algorithms, convolutional neural networks for image recovery and generation, generative models for image recovery, un-trained neural networks for signal recovery. The course ends with a brief outlook on how to apply those methods beyond image recovery for the recovery of a variety of other signals.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||4||Deep learning and inverse problems||Heckel, R. Klug, T.||
Learning and Teaching Methods
In the second session, we will discuss the method in more detail, go through a problem, or discuss a recent paper on that topic. If we discuss a paper, all students will need to read that paper before class so that we can have a meaningful discussion.
Description of exams and course work
Besides the written exam, 20% of the grade will be either through a presentation of a paper or through homework submission. In either case we will test whether students can analyse, evaluate, and design solvers for inverse problems with deep networks.
Specifically, if sufficiently few students are enrolled, each student has to present or defend a paper in the discussion session, and the evaluation of the paper, as well as the quality of presenting the arguments will count 20% towards the final grade.
If more students are enrolled than enabling each student to present a paper, then students will not present papers. In this case the submission of the homeworks which will contain evaluation and design of solvers for inverse problems will count 20% towards the final grade. The homework will contain analysis, evaluation, and design problems on solving inverse problems with deep networks.
The mode will be determined before the first lecture, and be communicated during the first lecture.
There is a possibility to take the exam in the following semester.