Deep Learning for Computer Vision Barcelona

Summer seminar UPC TelecomBCN (July 4-8, 2016)

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.

Course Instructors

Xavier Giro-i-Nieto Elisa Sayrol AmaiaSalvador Jordi Torres Eva Mohedano Kevin McGuinness
Xavier Giro-i-Nieto (XG) Elisa Sayrol (ES) Amaia Salvador (AS) Jordi Torres (JT) Eva Mohedano (EM) Kevin McGuinness (KM)

Teaching assistants

Junting Pan Míriam Bellver AlbertJimenez Andrea Ferri Alberto Montes Maurici Yagües
Junting Pan Míriam Bellver Albert Jiménez Andrea Ferri Alberto Montes Maurici Yagües


logo-etsetb logo-gpi logo-upc logo-bsc logo-insight logo-dcu
UPC ETSETB TelecomBCN UPC Image Processing Group Universitat Politecnica de Catalunya (UPC) Barcelona Supercomputing Center Insight Centre for Data Analytics Dublin City University (DCU)

Lecture Slides and Videos

Topic Speaker Slideshare YouTube
D1L1 Welcome XG Slides  
D1L2 Classification EM Slides  
D1L3 Deep networks ES Slides  
D1L4 Backward Propagation ES Slides Video
D1L5 Training EM Slides  
D1L6 Software Frameworks KM Slides Video
D2L1 Memory & Computation KM Slides Video
D2L2 Data Augmentation EM Slides  
D2L3 Visualization AS Slides Video
D2L4 Imagenet Challenge XG Slides  
D2L5 Transfer & Adaptation KM Slides Video
D2L6 Recurrent Networks XG Slides Video
D3L1 Unsupervised Learning KM Slides Video
D3L2 Saliency Prediction ES Slides  
D3L3 Optimization KM Slides  
D3L4 Object Detection AS Slides Video
D3L5 Face Recognition ES Slides Video
D3L6 Image retrieval EM Slides Video
D4L1 Generative Models KM Slides  
D4L2 Segmentation AS Slides Video
D4L3 Language and Vision XG Slides Video
D4L4 Video Analytics XG Slides Video
D4L5 Medical Imaging ES Slides Video
D4L6 Attention Models AS Slides Video
D5L Closing XG Slides  

Hands on TensorFlow

The seminar includes five practical sessions on TensorFlow, the Open Source Software Library for Machine Intelligence developed by Google. These sessions were taught by Professor Jordi Torres, with the teaching assistance of Maurici Yagües. Both of them are part of the Barcelona Supercomputing Center (BSC).

D1T Linear regressor Slides
D2T Clustering Slides
D3T Neuron & Tensorboard Slides
D4T CNN & SLIM Slides
D5T RNN Slides

The full course with code snippets is available in this repo.

Student projects

Master students together with some bachelor students organized in teams of five members who solved four directed tasks and developed an open project. The duration of the project corresponds to the single week of the course. Their slides and source code is available from their repos. If you are interested in hiring or contacting the students, some of them have provided their LinkedIn profiles from their project pages.

Team Project Page Slides Repo
Team 1 Character autorotation + Autoencoders Web Slides Repo
Team 2 Neural Style - Slides Repo
Team 3 Generative Adversarial Network - Slides Repo
Team 4 Multi-layer Neural Style - Slides Repo
Team 5 Deep Dream - Slides Repo


When Monday 4 Tuesday 5 Wednesday 6 Thursday 7 Friday 8
3:00-3:20 Welcome Memory Unsupervised Adversarial Project Expo 3
3:20-3:40 Classification Augmentation Saliency Segmentation Project Expo 4
3:40-4:00 Deep Visualization Optimization Language Project Expo 5
4:00-5:00 TensorFlow TensorFlow TensorFlow TensorFlow TensorFlow
4:00-5:00 Project Project Project Project Closing 3,4,5
5:00-5:20 Backpropagation ImageNet Objects Video Project Expo 1
5:20-5:40 Training Transfer Faces Medical Project Expo 2
5:40-6:00 Frameworks Recurrent Ranking Attention Break
6:00-7:00 Project Project Project Project Closing 1,2
6:00-7:00 TensorFlow TensorFlow TensorFlow JT TensorFlow JT TensorFlow


  • Course code: 230360 (Phd & master) / 230324 (Bachelor)
  • ECTS credits: 2.5 (Phd & master) / 2 (bachelor) (corresponds to full-time dedication during the week course)
  • Teaching language: English
  • The course is offered for both master and bachelor students, but under two study programmes adapted to each profile.
  • Class Dates: 4-8 July, 2016
  • Class Schedule: 3-7pm (you will need 6 extra hours a day for homework during the week course)
  • Capacity: 14 MSc students + 16 BSc students
  • Location: Campus Nord UPC, Module D5, Room 010


Registration is sold out for this edition of the seminar. The 30 available seats were covered by UPC TelecomBCN students.

We greatly appreciate the interest of several other students who could not register. We are planning a new edition of this seminar for June-July 2017. A new seminar on Deep Learning for Speech and Language is also planned for January 2017.

You are also encouraged to share your questions and solution in the public issues section for future reference and quality improvement of the course.

Video recordings

Sessions will be recorded in video and posted afterwards, together with the slides.


This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email

Find us at the class page.


This course is co-funded by the Erasmus+ programme from the European Union logo-erasmus
This course is supported by the NVdia GPU Center of Excellence at the Barcelona Supercomputing Center & Universitat Politecnica de Catalunya. logo-nvidia