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Advanced Deep Learning for Computer Vision

Module IN2364

This Module is offered by TUM Department of Informatics.

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.

Basic Information

IN2364 is a semester module in English language at Master’s level which is offered every semester.

This Module is included in the following catalogues within the study programs in physics.

  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
240 h 75 h 8 CP

Content, Learning Outcome and Preconditions

Content

- Recap of Neural Networks and CNNs
- Advanced Auto-encoders: Probabilistic approaches and the mathematical foundations (e.g., variational auto-encoders)
- Generative Adversarial Networks (from Goodfellow to CycleGANs and Progressive GANs)
- Autoregressive Networks and their parallelization
- Probabilistic vs deterministic generative methods
- Advanced RNNs and LSTMs (focus on math behind gradient propagation)
- Multi-dimensional CNNs: from audio to 3D scene environments; 3D vs multi-view CNNs, sparse CNNs (e.g., Octrees); spatio-temporal neural networks.
- CNNs on meshes: learning on structured and unstructured graphs. Mesh data structures and derived convolution operators on meshes using differential geometry.
- Reinforcement, Representation, and Deep Q Learning for Vision Problems; Deep Learning for Game AI (from Atari Games to Chess and Go, and ultimately Video Games)
- Bayesian DL (strong focus)
- Advanced Optimization and Hyperparameter tuning strategies
- Open Problems in Deep Learning for Computer Vision

Learning Outcome

Upon completion of this module, students will have acquired extensive theoretical concepts behind advanced architectures of neural networks, in particular in the context of computer vision tasks. In addition to the theoretical foundations, a significant aspect lies on the practical realization and training of neural networks.

Preconditions

MA0902 Analysis for Informatics
MA0901 Linear Algebra for Informatics

IN2346 Introduction to Deep Learning (expert knowledge required!)

This is the advanced lecture for deep learning with a specific focus on computer vision. Taking the “Introduction to Deep Learning” course is expected.

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

TypeSWSTitleLecturer(s)DatesLinks
VO 2 Advanced Deep Learning for Computer Vision (IN2364) Franzmann, A. Nießner, M. Wagner, S. Weitz, S. Mon, 10:00–12:00, MI 02.13.010
and singular or moved dates
eLearning
PR 3 Practical course: Advanced Deep Learning for Computer Vision (IN2364) Franzmann, A. Nießner, M. Wagner, S. Weitz, S. Fri, 14:00–16:00, MI 02.13.010
and singular or moved dates

Learning and Teaching Methods

The lectures will provide extensive theoretical aspects of neural networks and in particular deep learning architectures, specifically for advanced methods in the field of Computer Vision. The lecture will have reading assignments (e.g., from the DeepLearning book and recent CVPR/ICCV/ECCV papers).
The practical sessions 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.

Media

Projector, blackboard, PC

Literature

- Slides given during the course
- www.deeplearningbook.org

Module Exam

Description of exams and course work

- Written test of 60 minutes at the end of the course (for lecture)
- After each practical session, the students will have to provide the written working code to the teaching assistant for evaluation.
- In the written exam (40% of the final grade), we will ask questions regarding lecture theory
- In addition, to the written exam, the results of the projects will be evaluated (60% of the final grade); we will evaluate projects on a (bi-) weekly basis including reports (33.33%), oral presentations (33.33%), and code/submissions (33.33%).

Exam Repetition

There is a possibility to take the exam in the following semester.

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