Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)
Course 0000002290 in SS 2024
General Data
| Course Type | lecture |
|---|---|
| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Informatics 28 - Associate Professorship of Visual Computing (Prof. Nießner) |
| Lecturers |
Yujin Chen Ziya Erkoc Lei Li Matthias Nießner Barbara Rössle David Rozenberszki Susanne Weitz |
| Dates |
Wed, 08:00–12:00, MI 01.09.014 |
Assignment to Modules
-
IN2390: Advanced Deep Learning for Computer Vision: Visual Computing / Advanced Deep Learning for Computer Vision: Visual Computing
This module is included in the following catalogs:- Catalogue of non-physics elective courses
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 + VisualizationLecture 2: Siamese NetworksLecture 3: Autoencoders & Self-supervised Learning + Advanced Architectures (U-Net)Lecture 4: Representation LearningLecture 5: Sequence ModelsLecture 6: Generative Models: GANs 1Lecture 7: Generative Models: GANs 2Lecture 8: Diffusion ModelsLecture 9: Graph Neural NetworksLecture 10: Multi-DimensionalLecture 11: Neural FieldsLecture 12: NeRFLecture 13: Generative NeRFsThe 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 https://niessnerlab.org/ 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)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| WS 2023/4 | Advanced Deep Learning for Computer Vision: Visual Computing (IN2390) | Erkoc, Z. Li, L. Nießner, M. Rössle, B. Rozenberszki, D. … (total 6) |
Wed, 10:00–12:00, MI 01.09.014 |
| SS 2023 | Advanced Deep Learning for Computer Vision: Visual Computing (IN2390) | Chen, Z. Franzmann, A. Nie, Y. Nießner, M. Rössle, B. … (total 6) |
Wed, 10:00–12:00, MI 01.09.014 |
| WS 2022/3 | Advanced Deep Learning for Computer Vision: Visual Computing (IN2390) | Nießner, M. |
Wed, 10:00–12:00, virtuell |
| SS 2022 | Advanced Deep Learning for Computer Vision: Visual Computing (IN2390) | Nießner, M. |
Wed, 10:00–11:00, virtuell |
| WS 2021/2 | Advanced Deep Learning for Computer Vision: Visual Computing (IN2390) | Nießner, M. |
Mon, 10:00–12:00, virtuell |