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Closing, Course Offer 17/18 & Homework (D5 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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Closing, Course Offer 17/18 & Homework (D5 2017 UPC Deep Learning for Computer Vision)

  1. 1. Day 5 Closing, Course offer 17/18 & Homework #DLUPC [course site]
  2. 2. 2 Thank you Eduard Ramon Marta Coll Fran Roldan
  3. 3. 3
  4. 4. 4 Deep learning opportunities at UPC TelecomBCN during 2017/2018 year: Master MET BSc Deep Learning (5 ECTS) Autumn Semester 2017 Spring Semester 2018 Deep Learning for Speech, Audio & Language (2.5 ECTS) Intro to Deep Learning (2 ECTS) Deep Learning for Computer Vision (2.5 ECTS) Introduction to Research (5,10,15 ECTS) Reading Groups on AI & Biomedical Imaging (2.5 ECTS) Bachelor Thesis (12, 24 ECTS) Master Thesis (30 ECTS)
  5. 5. 5 Deep learning opportunities during 2017/2018 year: Learn more @ ETSETB TelecomBCN Master MIRI, Industry, Visitors... Deep Learning (5 ECTS) Autumn Semester 2017 Spring Semester 2018 Deep Learning for Speech, Audio & Language (2.5 ECTS) Intro to Deep Learning (2 ECTS) Deep Learning for Computer Vision (2.5 ECTS)
  6. 6. 6 Deep learning opportunities during 2017/2018 year: Learn more @ ETSETB TelecomBCN Master MET BSc Autumn Semester 2017 Spring Semester 2018 Reading Groups on AI & Biomedical Imaging (2.5 ECTS)
  7. 7. 7 ● Reading & discussion group (DLMI) ● E-mail to veronica.vilaplana@upc.edu if you want to join BSc, MSc & Phd on biomedical imaging applications Learn more @ ETSETB TelecomBCN
  8. 8. 8 ● Reading Group with public listing of videos, slides and papers. ● E-mail to xavier.giro@upc.edu if you want to join in Autumn 2017. Learn more @ ETSETB TelecomBCN
  9. 9. 9 Deep learning specific courses during 2017/2018 year: Learn more @ UPC TelecomBCN Master MET BSc Autumn Semester 2017 Spring Semester 2018 Introduction to Research (5,10,15 ECTS) Bachelor Thesis Master Thesis
  10. 10. Vision (GPI-UPC) Speech & Language (TALP-UPC) Xavier Giró Elisa Sayrol Verónica Vilaplana Ramon Morros Javier Ruiz Marta Ruiz Costa- jussà Antonio Bonfaonte Javier Hernando José Adrián Rodríguez Fonollosa https://imatge.upc.edu http://www.talp.upc.edu/
  11. 11. 11 Learn more @ GPI UPC 11 Professors / Associate Professors 7 Phd students 2 Technical support https://imatge.upc.edu/web/
  12. 12. 12 Visual Reasoning Learn more @ GPI UPC Johnson, Justin, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick. "CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning." CVPR 2017
  13. 13. 13 Gaze Scanpath for Saliency Prediction (2D & 360o images) Learn more @ GPI UPC Output Saliency Volume Scan-paths Conv Max Pooling Upsampling Sigmoid Sampling
  14. 14. 14 Learn more @ GPI UPC X. Lin, Campos, V., Giró-i-Nieto, X., Torres, J., and Canton-Ferrer, C., “Disentangling Motion, Foreground and Background Features in Videos”, in CVPR 2017 Workshop Brave New Motion Representations kernel dec C3D Foreground Motion First Foreground Background Fg Dec Bg Dec Fg Dec Reconstruction of foreground in last frame Reconstruction of foreground in first frame Reconstruction of background in first frame uNLC Mask Block gradients Last foreground Kernels share weights
  15. 15. 15 Image synthesis with Generative Adversarial Networks. Learn more @ GPI UPC Shrivastava, Ashish, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb. "Learning from simulated and unsupervised images through adversarial training." arXiv preprint arXiv:1612.07828 (2016).
  16. 16. 16 Wearables, Lifelogging & Egocentric Vision Learn more @ GPI UPC
  17. 17. 17 Affective Computing CNN Learn more @ GPI UPC
  18. 18. 18 Medical Imaging Segmentation Learn more @ GPI UPC Brain tumor Infant brain (WM/ GM/ CSF) White matter hyperintensitiesLiver tumor
  19. 19. 19 Generative adversarial networks in medical imaging Synthesis Super-resolution Learn more @ GPI UPC CT from MRI High resolution 3D cardiac MRI
  20. 20. 20 Alzheimer’s Disease: prediction of preclinical AD (collaboration with Pasqual Maragall Foundation) Histological tissue: Classification / Feature extraction (collaboration with Centre for Genomic Regulation) Learn more @ GPI UPC Associate tissue samples with histological and pathological phenotypes
  21. 21. 21 Learn more @ GPI UPC Multimodal People Recognition Incremental Learning
  22. 22. 22 Learn more @ GPI UPC “Dancing” with Deep Learning”, generating choreographies, using LSTM and Mixture Density Models) ...our skeleton is still working
  23. 23. 23 3D point cloud analysis SEMANTIC SEGMENTATION SUPER-RESOLUTION Learn more @ GPI UPC
  24. 24. 24 Learn more @ Insight DCU
  25. 25. 25 BSc & Master MET BSc Data Analysis and Machine Learning (7.5 ECTS) Autumn Semester 2017 Spring Semester 2018 Bachelor Thesis Master Thesis Learn more @ Insight DCU Master MET
  26. 26. Dublin City UniversityInsight Centre for Data Analytics 26 Learn more @ Insight DCU Kevin McGuinness Noel O’Connor Cathal Gurrin Alan E. Smeaton
  27. 27. 27 Learn more @ Insight DCU
  28. 28. ● Multi-task deep learning ● Learning generic representations ● Unsupervised and semi-supervised feature learning ● Visual attention models and applications ● Image segmentation ● Interactive computer vision ● Multimedia recommender systems (hybrid content based and collaborative) ● Deep video analysis (tagging, genres, actions) ● Generative adversarial networks ● Model update and lifelong learning 28 Learn more @ Insight DCU
  29. 29. Some applications: ● Image and video retrieval ● Medical imaging and computer aided diagnosis ● Lifelogging ● Autonomous vehicles ● Crowd scene analysis ● Brand and logo detection ● Photo OCR 29 Learn more @ Insight DCU
  30. 30. 30 Master in Computer Vision (one and two-year tracks). Opportunities @ UPC+UAB+UPF+UOC
  31. 31. 31 Learn more Grup d’estudi de machine learning Barcelona
  32. 32. Learn more online 32[course site] [course site] Self-paced online learning (video & slides available). [course site]
  33. 33. 33 Deep Learning labs with TensorFlow and Keras by Amaia Salvador & Santi Pascual. Learn more online
  34. 34. 34 http://cs231n.stanford.edu/ Learn more online
  35. 35. 35 http://cs224n.stanford.edu/ Learn more online
  36. 36. 36 Learn more online Electronic Frontier Foundation: Measuring the Progress of AI Research
  37. 37. 37 Learn more online
  38. 38. 38 Learn more online @DocXavi @kevinmcguinness @amaiasalvador @ElisaSayrol @ramonmorros_upc @Javier_RuizHida #DLUPC
  39. 39. 39 Learn more online “Machine learning” sub-Reddit.
  40. 40. 40 Learn more online
  41. 41. 41 Computer vision is (finally) taking off... ...because machines have learned to see. Learning only to see ?
  42. 42. Learning only to see ? Nexi, del MIT Media Lab (Foto: Spencer Lowel) 42
  43. 43. Video games Learning only to see ? Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013). 43
  44. 44. Human games Learning only to see ? 44 Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M. and Dieleman, S., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), pp.484-489
  45. 45. Autonomous Driving Google Self-driving car Learning only to see ? 45
  46. 46. Elgammal, Ahmed, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. "CAN: Creative Adversarial Networks, Generating" Art" by Learning About Styles and Deviating from Style Norms." arXiv 2017. Learning only to see ? 46 Visual arts
  47. 47. Movie Scripts Learning only to see ? 47 Ars Technica, “Movie written by algorithm turns out to be hilarious and intense” (2016)
  48. 48. Public Health Esteva, Andre, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun. "Dermatologist-level classification of skin cancer with deep neural networks." Nature 542, no. 7639 (2017): 115-118. Learning only to see ? 48
  49. 49. Nacho Hernandez, “Why artificial intelligence will democratize healthcare” (TEDx Talk, 2014) Public health Learning only to see ? 49
  50. 50. Nancy Lublin, “The heartbreaking text that inspired a crisis helpline” (TED Talk 2015) Mental health Learning only to see ? 50
  51. 51. Psychological support and counseling ? Learning only to see ? 51
  52. 52. 52 Affective computing Rana el Kalioubi, “This app know how you feel, from the look on your face”, TEDTalks 2015. Learning only to see ?
  53. 53. 53 Nexi Project, from MIT Media Lab (Photos: Spencer Lowel) [video] Affective computing Learning only to see ?
  54. 54. 54 Challenges
  55. 55. 55 Xavier Sala-i-Martin (Columbia University), “Les conclusions del Fòrum de Davos” (TV3, 03/02/2016) - in Catalan Carles Boix (Princeton University), “La quarta revolució industrial” (Diari Ara, 08/02/2016) - in Catalan Artificial intelligence
  56. 56. “Google’s chairman (Eric Schmidth) thinks artificial intelligence will let scientists solve some of the world’s "hard problems," like population growth, climate change, human development, and education.” (Bloomberg Business, 11/01/2016) [+info @ MIT Technology Review] Artificial intelligence 56
  57. 57. Google’s CEO Sundar Pichai: “Era Of Computers Will End Very Soon, AI Will Rule” (Fossbytes, 03/05/2016) Artificial intelligence 57
  58. 58. 58 Barack Obama, Neural Nets, Self-driving cars, and the Future of the World (Wired, June 2016) Artificial intelligence
  59. 59. Artificial intelligence Stephen Hawking, “Artificial intelligence could spell out the human race.” (2014) 59
  60. 60. Jeremy Howard, “The wonderful and terrifying implications of computers that can learn”, TEDTalks 2014. Artificial intelligence 60
  61. 61. 61 The White House: “How to prepare the future for the Future Intelligence” (Jun’16) “Artificial Intelligence, Autonomy, and the Economy” (Dec’16) “These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place.” ArtificiaI Intelligence & Human Ethics
  62. 62. 62 Kai-Fu Lee, “The Real Threat of Artificial Intelligence”. The New York Times (24/06/2017) ArtificiaI Intelligence & Human Ethics Figure: Rune Fisker “...leading to unprecedented economic inequalities and even altering the global balance of power.”
  63. 63. 63 ArtificiaI Intelligence & Human Ethics
  64. 64. 64 ArtificiaI Intelligence & Human Ethics
  65. 65. 65 ArtificiaI Intelligence & Human Ethics
  66. 66. 66 ArtificiaI Intelligence & Human Ethics
  67. 67. Big data Internet of things - IoT 67 ArtificiaI Intelligence & Human Ethics
  68. 68. Personal data Big data 68 ArtificiaI Intelligence & Human Ethics
  69. 69. Neil Lawrence, OpenAI won’t benefit humanity without open data sharing (The Guardian, 14/12/2015) Phd Comics: “Who owns your data ? (Hint: it is not you)” 69 ArtificiaI Intelligence & Human Ethics
  70. 70. 70 ArtificiaI Intelligence & Human Ethics 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
  71. 71. 71 ArtificiaI Intelligence & Human Ethics
  72. 72. 72
  73. 73. Course photo at the stairs (exit + left)

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