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Welcome (D1L1 2017 UPC Deep Learning for Computer Vision)

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https://telecombcn-dl.github.io/2017-dlcv/

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed 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 image captioning.

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Welcome (D1L1 2017 UPC Deep Learning for Computer Vision)

  1. 1. [course site] Xavier Giro-i-Nieto xavier.giro@upc.edu Associate Professor Universitat Politecnica de Catalunya Technical University of Catalonia Welcome Day 1 Lecture 1 #DLUPC
  2. 2. 2 Instructors Xavier Giro-i-Nieto • Web: https://imatge.upc.edu/web/people/xavier-giro Associate Professor at Universitat Politecnica de Catalunya (UPC) Escola d’Enginyeria de Terrassa UPC - ESEIAAT
  3. 3. 3 Kevin McGuinness Research Fellow at Dublin City University (DCU) SFI Funded Starting Investigator Insight Centre for Data Analytics http://www.eeng.dcu.ie/~mcguinne/ Instructors
  4. 4. 4 Amaia Salvador PhD Candidate Image Processing Group (GPI), Universitat Politècnica de Catalunya https://imatge.upc.edu/web/people/amaia-salvador Instructors
  5. 5. 5 Elisa Sayrol Instructors Associate Professor at Universitat Politècnica de Catalunya (UPC) Web: https://imatge.upc.edu/web/people/elisa-sayrol
  6. 6. 6 Èric Arazo Phd student at Dublin City University (DCU) Insight Centre for Data Analytics Instructors
  7. 7. 7 Ramon Morros Instructors Associate Professor at Universitat Politècnica de Catalunya (UPC) Web: https://imatge.upc.edu/web/people/josep-ramon-morros Escola d’Enginyeria de Terrassa UPC - ESEIAAT
  8. 8. 8 Verónica Vilaplana Instructors Associate Professor at Universitat Politècnica de Catalunya (UPC) Escola d’Enginyeria de Terrassa UPC - ESEIAAT • Web: https://imatge.upc.edu/web/people/veronica-vilaplana
  9. 9. 9 Javier Ruiz Hidalgo Instructors Associate Professor at Universitat Politècnica de Catalunya (UPC) • Web: https://imatge.upc.edu/web/people/javier-ruiz-hidalgo Escola d’Enginyeria de Terrassa UPC - ESEIAAT
  10. 10. 10 Management Instructor Area Xavier Giró Coordination Kevin McGuinness Lectures Elisa Sayrol Logistics & Evaluation Xavier Giró Lab Amaia Salvador Project Xavier Giró Web and online material
  11. 11. 11 Guest lectures Everybody must sign up for the last day here, and promote it !
  12. 12. 12 Teaching assistants Eduard Ramon Marta Coll Fran Roldan
  13. 13. 13 Acknowledgments Eva Mohedano Santiago Pascual Jose Adrián Rodríguez Fonollosa Marta Ruiz Costa-jussà Antonio Bonafonte Javier Hernando Alan F. Smeaton Noel E. O’Connor Jordi Torres Ferran Marqués
  14. 14. 14 Densely linked slides
  15. 15. 15 Motivation Source: 50 Best jobs in America (Glassdoor) The best job in the USA: Data scientist.
  16. 16. 16 The Economist, “Million-dollar babies” (02/04/2016) Motivation
  17. 17. 17 Nature, “AI talent grab sparks excitement and concern” (26/04/2016) Motivation
  18. 18. 18 Annual Conference on Neural Information Processing Systems (NIPS) @ Barcelona (2016) Motivation
  19. 19. 19 Ian Goodfellow and Yoshua Bengio and Aaron Courville, “Deep Learning”. MIT Press 2016. Motivation
  20. 20. 20 Motivation [course site][course site][course site]
  21. 21. 21 Motivation [course site]
  22. 22. 22 Motivation “NVIDIA to train 100,000 Developers in Deep Learning in 2017.”
  23. 23. 23 Motivation Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017. The AI Hype ALGORITHMS Deep Learning BIG DATA Vision: ImageNet BIG COMPUTATION GPUs
  24. 24. 24 Motivation Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017. The AI Hype ALGORITHMS Deep Learning BIG DATA Vision: ImageNet BIG COMPUTATION GPUs
  25. 25. 25
  26. 26. 26 Motivation Hubel, David H., and Torsten N. Wiesel. "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex." The Journal of physiology 160, no. 1 (1962): 106-154. Hubel, David H., and Torsten N. Wiesel. "Receptive fields and functional architecture of monkey striate cortex." The Journal of physiology 195, no. 1 (1968): 215-243. Inspiration from Neuroscience: Hierarchical model of the visual pathway with neurons responding to: ● oriented edges and bars @ lower areas ● specific stimuli @ higher areas
  27. 27. 27 Motivation Fukushima, Kunihiko, and Sei Miyake. "Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition unaffected by a shift in position." In Competition and cooperation in neural nets, pp. 267-285. Springer Berlin Heidelberg, 1982. Hierarchical model applied to a neural network: ● alternating layers of simple and complex cells. ● down sampling ● shift invariance (convolutions)
  28. 28. 28 Motivation Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. "Learning representations by back-propagating errors." Cognitive modeling 5, no. 3 (1988). Training a neural network with the back-propagation algorithm (backprop).
  29. 29. 29 Motivation LeCun, Yann, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, and Lawrence D. Jackel. "Backpropagation applied to handwritten zip code recognition." Neural computation 1, no. 4 (1989): 541-551. Hierarchical neural model + backpropagation.
  30. 30. 30 Motivation Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9, no. 8 (1997): 1735-1780.
  31. 31. 31 Motivation Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." NIPS 2012 12,573 citations (June 2017)
  32. 32. 32 Motivation Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
  33. 33. 33 Motivation Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017. The AI Hype ALGORITHMS Deep Learning BIG DATA Vision: ImageNet BIG COMPUTATION GPUs
  34. 34. 34 Motivation Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image database." CVPR 2009.
  35. 35. 35 Motivation Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017. The AI Hype ALGORITHMS Deep Learning BIG DATA Vision: ImageNet BIG COMPUTATION GPUs
  36. 36. 36 Motivation
  37. 37. 37 Motivation Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017. The AI Hype ALGORITHMS Deep Learning BIG DATA Vision: ImageNet BIG COMPUTATION GPUs
  38. 38. 38 What’s new this 2017 ? ● From 2 to 5 instructors from UPC Image Processing Group. ● Labs provided by NVIDIA Deep Learning Institute. ● Two GPUs per each team on GCloud. ● Projects based on the “Nuts & Bolts” tutorial by Andrew Ng. ● Public presentations of students projects. ● Two guest speakers: Àgata Lapedriza & Elisenda Bou. ● DLCV 2016 and DLSL 2017 videos are available to review.
  39. 39. 39 Discussion https://piazza.com/upc/summer2017/230360/home Pose your logistic questions (only of interest for course participants) on this private course management platform:
  40. 40. 40 Discussion https://www.reddit.com/r/dlupc/ Pose your technical questions (of broad interest) on this public subredit (similar to Stanford cs231n’s):
  41. 41. 41 http://bit.ly/dlcv2017 Course material First on Piazza, later on the public site.
  42. 42. 42 MSc BSc Guests 3:00- 4:00 Lectures @ D5-010 4:00- 5:00 Lectures @ D5-010 5:00- 6:00 Labs @ D5-004 Labs @ D5-005 6:00- 7:00 Project @ D5-004 & -007 (optional) Project Wednesday to Monday @ D5 Tuesday @ B3-Sala Teleensenyament (2nd floor) MSc BSc Guests General public 3:00- 5:00 Project presentations 5:00- 5:40 Guest Lecture: Àgata Lapedriza 5:40- 6:20 Guest Lecture: Elisenda Bou 6:20- 7-00 Closing Project Schedule
  43. 43. 43 Grading BSc MSc Online Tests 60% 30% Lab 30% 30% Project - 30% Communication - 10% Attendance 10% -10% x miss day Reddit +10% (if valuable) +10% (if valuable)
  44. 44. 44 Grading: Online tests [Online test preview]
  45. 45. Questions?

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