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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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teaching:ws2019:cvx4cv [2019/10/10 12:04]
Zhenzhang Ye
teaching:ws2019:cvx4cv [2020/06/11 00:43]
wuta
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 <html> <html>
-<b> Announcement: <br />  <br /> +<b> Announcement: </b><br /> 
-<html><font color="red">The first lecture starts on 21.10. There will be NO lectures on 14.10 and 16.10. </font></b>+<b><font color="red"> The retake exam has been re-scheduled on July 8. </font></b> 
 +<br /> 
 +The retake exam is unfortunately canceled due to the Coronavirus outbreak. 
 +<br /> 
 +The location of the final exam is updated. <br /> 
 +There is no lecture on 23.12. The tutorial session on 08.01 will be replaced by the lecture.  
 +<br /> 
 +There will be no lecture/tutorial on 09.12 and 11.12 due to NeurIPS conference. <br /> 
 +The tutorial on 30.10 will be given by Yuesong Shen 
 +<br /> 
 +The first lecture takes place on 21.10. There will be no lectures on 14.10 and 16.10. 
 </html> \\ </html> \\
  
Line 18: Line 28:
  
 Elements in convex analysis Elements in convex analysis
-       * Convex sets and convex functions+       * Convex set and convex function
        * Existence and uniqueness of minimizers        * Existence and uniqueness of minimizers
-       Subdifferentials +       Subdifferential 
-       * Convex conjugates +       * Convex conjugate 
-       Saddle point problems and duality+       Duality theory 
 +       * Moreau envelope
      
 Numerical methods Numerical methods
        * Gradient-based methods        * Gradient-based methods
-       * Proximal algorithmsprimal-dual hybrid gradient method, alternating direction method of multipliers +       * Proximal algorithmsprimal-dual hybrid gradient method, alternating direction method of multipliers 
-       Convergence analysis+       Unified convergence analysis
        * Acceleration techniques        * Acceleration techniques
- 
  
  
 Examplary applications in machine learning and computer vision include Examplary applications in machine learning and computer vision include
-       * Training of SVMs, Logistic regression +       * Logistic regression 
-       * Image reconstruction (e.g. denoising, deblurring, inpainting)+       * Training of SVMs 
 +       * Image reconstruction (e.g. denoising, inpainting, segmentation)
        * Low-rank and sparse matrix decomposition        * Low-rank and sparse matrix decomposition
-We will implement some of them in MATLAB.+We will implement some of them in Python and MATLAB.
  
 ---- ----
Line 42: Line 53:
 == Lecture == == Lecture ==
 ** Location:** Room 02.09.023\\ ** Location:** Room 02.09.023\\
-** Time and Date:** Monday 16:15 - 18:00\\   +** Time and Date:** Monday 16:15 -- 18:00\\   
 ** Start:** October 21st, 2019 \\ ** Start:** October 21st, 2019 \\
 ** Lecturer:** [[:members:wuta|Dr. Tao Wu]]\\ ** Lecturer:** [[:members:wuta|Dr. Tao Wu]]\\
Line 49: Line 60:
 == Exercise == == Exercise ==
 ** Location:** Room 02.09.023\\  ** Location:** Room 02.09.023\\ 
-** Time and Date:** Wednesday 12:15 - 14:00\\   +** Time and Date:** Wednesday 12:15 -- 14:00\\   
 ** Start:** October 23rd, 2019\\ ** Start:** October 23rd, 2019\\
 ** Organization:** [[:members:yez|Zhenzhang Ye]]\\  ** Organization:** [[:members:yez|Zhenzhang Ye]]\\ 
Line 67: Line 78:
 1. get at least 75% grades of all exercises totally, i.e. sum of the grades you get in all exercises divided by the grades of all exercises (without bonus) should be >= 0.75\\ 1. get at least 75% grades of all exercises totally, i.e. sum of the grades you get in all exercises divided by the grades of all exercises (without bonus) should be >= 0.75\\
 2. present your theoretical solution during tutorial at least once in this semester.\\ 2. present your theoretical solution during tutorial at least once in this semester.\\
-You cannot improve either 1.0 or >4.0.\\+You cannot improve either 1.0 or 5.0.\\
  
 == Exam == == Exam ==
-tba+Date: February 28th, 17:00 - 18:30. \\ 
 +Place: MW 0350, Egbert-von-Hoyer-Hörsaal (5503.EG.350). \\ 
 +The final exam will be written. You are allowed to bring one A4-page, handwritten cheat sheet to the exam. 
 + 
 +== Retake Exam ==
  
  
-== Lecture Material == +== Lecture Materials == 
-Course material (slides and exercise sheets) can be accessed [[teaching:ws2019:cvx4cv:materials|here]]. \\ \\ Send us an email if you need the password.+Course materials (slides and exercise sheets/solutions) can be accessed [[teaching:ws2019:cvx4cv:materials|here]]. \\ \\ Send us an email if you need the password.

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More