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Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)

Course 0000002290 in SS 2023

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 28 - Associate Professorship of Visual Computing (Prof. Nießner)
Lecturers Zhenyu Chen
Assia Franzmann
Yinyu Nie
Matthias Nießner
Barbara Rössle
David Rozenberszki
Dates Wed, 10:00–12:00, MI 01.09.014

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks Note, this lecture is closely related (and mutually exclusive) to the lecture “Advanced Deep Learning for Computer Vision: Dynamic Vision”. The two lectures share some theoretical content, but the “Visual Computing” module provides a clear focus on visual computing tasks, which is especially important for the practical part in the form of a semester-long project. Lecture content: Lecture 1: I2DL Recap, DL best practices Recap + Visualization Lecture 2: Siamese Networks Lecture 3: Autoencoders & Self-supervised Learning + Advanced Architectures (U-Net) Lecture 4: Representation Learning Lecture 5: Sequence Models Lecture 6: Generative Models: GANs 1 Lecture 7: Generative Models: GANs 2 Lecture 8: Diffusion Models Lecture 9: Graph Neural Networks Lecture 10: Multi-Dimensional Lecture 11: Neural Fields Lecture 12: NeRF Lecture 13: Generative NeRFs The semester-long project will be key, students shall get familiar with Deep Learning through hours of training and testing. They will work with PyTorch and implement advanced network architectures. We recommend to take a look at the recent list of publications at to get a better idea of recent research projects.
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

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