Downloaded 31 times
![bit.ly/commsenselab
@DocXavi
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● 1,000 object classes
(categories).
● Images:
○ 1.2 M train
○ 100k test.
ImageNet Challenge
Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet large
scale visual recognition challenge." International Journal of Computer Vision 115, no. 3 (2015): 211-252. [web]](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-31-2048.jpg)
![bit.ly/commsenselab
@DocXavi
32
ImageNet Challenge
Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet large
scale visual recognition challenge." International Journal of Computer Vision 115, no. 3 (2015): 211-252. [web]
Slide credit:
Rob Fergus (NYU)
-9.8%
Based on SIFT + Fisher Vectors](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-32-2048.jpg)
![bit.ly/commsenselab
@DocXavi
41Slide concept: Perronin, F., Tutorial on LSVR @ CVPR’14, Output embedding for LSVR
One-hot Representation
[1,0,0]
[0,1,0]
[0,0,1]](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-41-2048.jpg)
![59
#StackGAN Zhang, Han, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas.
"Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks." ICCV 2017. [code]
Image Synthesis](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-59-2048.jpg)
![bit.ly/commsenselab
@DocXavi
84
Beyond Multimedia
#AlphaFold Evans, R., J. Jumper, J. Kirkpatrick, L. Sifre, T. F. G. Green, C. Qin, A. Zidek et al. "De novo structure prediction
with deeplearning based scoring." Annu Rev Biochem 77 (2018): 363-382. [blog]](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-84-2048.jpg)
![bit.ly/commsenselab
@DocXavi
92
The AI Hype (?)
CVPR is the top conference in
computer science
(IN ALL COMPUTER SCIENCE !!)
Again
(IN ALL COMPUTER SCIENCE !!)
...and Top #2 & #4 are on
machine learning
...as Top #3 & #5 are on
computer vision.
Source: David Forsyth
@ Good Citizen CVPR 2018 [video] [slides]](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-92-2048.jpg)
![bit.ly/commsenselab
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Deep Learning courses @ UPC TelecomBCN:
● MSc course [2017] [2018] [2019]
● BSc course [2018] [2019]
● 1st edition (2016)
● 2nd edition (2017)
● 3rd edition (2018)
● 4th edition (2019)
● 1st edition (2017)
● 2nd edition (2018)
● 3rd edition - NLP (2019)
Next edition: Autumn 2019 Included in ETSETB Master MATT Deep Learning Track + MET](https://image.slidesharecdn.com/x-thesesdeeplearningrepresentationsforall-190702100036/75/Deep-Learning-Representations-for-All-a-ka-the-AI-hype-106-2048.jpg)
Uploaded byUniversitat Politècnica de Catalunya
Deep Learning Representations for All (a.ka. the AI hype)
The document discusses the advancements in deep learning and its application in various fields such as computer vision, speech recognition, and multimedia processing. It highlights the roles of significant research centers and methodologies, including neural networks and the back-propagation algorithm, while also addressing challenges like data access and social biases in AI. The content includes references to influential works and researchers, showcasing the evolution and impact of deep learning technologies.
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Deep Learning Representations for All (a.ka. the AI hype)
- 1. bit.ly/commsenselab @DocXavi Deep Learning Representations forAll (a.k.a. the AI hype) Xavier Giro-i-Nieto @DocXavi xavier.giro@upc.edu
- 2. bit.ly/commsenselab @DocXavi 2 Xavier Giro-i-Nieto Associate Professorat Universitat Politècnica de Catalunya (UPC) Hola IDEAI Center for Intelligent Data Science & Artificial Intelligence
- 3. bit.ly/commsenselab @DocXavi 3 ● 11 facultymembers ● 12 Phd students Research Group & Centers https://imatge.upc.edu/ https://www.bsc.es/ ● National computation center #1 ● Supercomputer MareNostrum ● Emerging Technologies for Artificial Intelligence Group, directed by Prof. Jordi Torres. https://ideai.upc.edu/ ● Center funded in 2017 ● 60 researchers IDEAI (Intelligent Data Science and Artificial Intelligence)
- 4. bit.ly/commsenselab @DocXavi 4 Why am Ihere ? Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
- 5. bit.ly/commsenselab @DocXavi 5 Why am Ihere ? Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
- 6. bit.ly/commsenselab @DocXavi 6 Why am Ihere ? ● Old style machine learning: ○ Engineer features (by some unspecified method) ○ Create a representation (descriptor) ○ Train shallow classifier on representation ● Example: ○ SIFT features (engineered) ○ BoW representation (engineered + unsupervised learning) ○ SVM classifier (convex optimization) ● Deep learning ○ Learn layers of features, representation, and classifier in one go based on the data alone ○ Primary methodology: deep neural networks (non-convex) Data Pixels Deep learning Convolutional Neural Network Data Pixels Low-level features SIFT Representation BoW/VLAD/Fisher Classifier SVM EngineeredUnsupervisedSupervised Supervised
- 7.
- 8.
- 9.
- 10. bit.ly/commsenselab @DocXavi 10 DL basic unit:The Perceptron The Perceptron is seen as an analogy to a biological neuron, because it fire an impulse once the sum of all inputs is over a threshold. Minsky, Marvin, and Seymour A. Papert. Perceptrons: An introduction to computational geometry. 1969
- 11. bit.ly/commsenselab @DocXavi 11 DL basic unit:The Perceptron A single perceptron can only define linear decision boundaries. x2 Class 0 Class 1 2D input space data x
- 12. bit.ly/commsenselab @DocXavi 12 Non-linear decision boundaries Realworld data often needs a non-linear decision boundary ● Images ● Audio ● Text
- 13.
- 14. bit.ly/commsenselab @DocXavi 14 ● Needs a“finite number of hidden neurons”: finite may be extremely large ● How to find the parameters (weights, biases) of these neurons ?
- 15. bit.ly/commsenselab @DocXavi 15 Multilayer Perceptron (MLP) Inpractice, deep neural networks nets can usually represent more complex functions with less total neurons (and therefore, less parameters)
- 16. bit.ly/commsenselab @DocXavi 16 How to findthe parameters ? 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.
- 17. bit.ly/commsenselab @DocXavi 17 How to learna memory unit ? Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9, no. 8 (1997).
- 18. bit.ly/commsenselab @DocXavi 18 How to reuseneurons ? Fully Connected layer (FC) Convolutional layer (Conv) Figures: Ranzatto
- 19. bit.ly/commsenselab @DocXavi 19 Convolutional Neural Network(CNN) #LeNet-5 LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
- 20. bit.ly/commsenselab @DocXavi 20 Many other researchershave also contrinbuted to the field as, for example, those pointed out by LSTM author Jürgen Schmidhuber in “Deep Learning Conspirancy”.
- 21. bit.ly/commsenselab @DocXavi 21 Why am Ihere ? Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
- 22. bit.ly/commsenselab @DocXavi 22 Big data forVision: ImageNet ● 1,000 object classes (categories). ● Images: ○ 1.2 M train ○ 100k test. Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image database." CVPR 2009.
- 23. bit.ly/commsenselab @DocXavi 23 Data Challenge: SocialBiases #Equalizer Burns, Kaylee, Lisa Anne Hendricks, Trevor Darrell, and Anna Rohrbach. "Women also Snowboard: Overcoming Bias in Captioning Models." ECCV 2018.
- 24. bit.ly/commsenselab @DocXavi 24 Data Challenge: Dataaccess Personal data Internet of things - IoT Neil Lawrence, OpenAI won’t benefit humanity without open data sharing (The Guardian, 2015)
- 25. bit.ly/commsenselab @DocXavi 25 Why am Ihere ? Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
- 26.
- 27. bit.ly/commsenselab @DocXavi 27 Computation Deng, Jia, WeiDong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image database." CVPR 2009.
- 28. bit.ly/commsenselab @DocXavi 28 Computation challenge Deng, Jia,Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image database." CVPR 2009.
- 29. bit.ly/commsenselab @DocXavi 29 Why am Ihere ? Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
- 30. bit.ly/commsenselab @DocXavi 30 Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." NIPS 2012 12,573 citations (June 2017) 28,366 (Sep 2018) 42,077 (Jun 2019)
- 31. bit.ly/commsenselab @DocXavi 31 ● 1,000 objectclasses (categories). ● Images: ○ 1.2 M train ○ 100k test. ImageNet Challenge Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet large scale visual recognition challenge." International Journal of Computer Vision 115, no. 3 (2015): 211-252. [web]
- 32. bit.ly/commsenselab @DocXavi 32 ImageNet Challenge Russakovsky, Olga,Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet large scale visual recognition challenge." International Journal of Computer Vision 115, no. 3 (2015): 211-252. [web] Slide credit: Rob Fergus (NYU) -9.8% Based on SIFT + Fisher Vectors
- 33.
- 34. bit.ly/commsenselab @DocXavi 34 ImageNet Image Recognition ElectronicFrontier Foundation: “Measuring the Progress of AI Research” (2017)
- 35. bit.ly/commsenselab @DocXavi 35 Learning Representations Jitendra Malik,“What lead computer vision to deep learning ?” ACM Communications 2017.
- 36.
- 37.
- 38.
- 39.
- 40. bit.ly/commsenselab @DocXavi 40 Encoder 0 1 0 Cat A Krizhevsky, ISutskever, GE Hinton “Imagenet classification with deep convolutional neural networks” NIPS 2012
- 41. bit.ly/commsenselab @DocXavi 41Slide concept: Perronin,F., Tutorial on LSVR @ CVPR’14, Output embedding for LSVR One-hot Representation [1,0,0] [0,1,0] [0,0,1]
- 42.
- 43.
- 44. bit.ly/commsenselab @DocXavi 44 Decoder Radford, Alec, LukeMetz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." ICLR 2016. #DCGAN 0 1 0 Cat Fig: Xudong Mao #DCGAN
- 45.
- 46.
- 47.
- 48. bit.ly/commsenselab @DocXavi 48 Isola, Phillip, Jun-YanZhu, Tinghui Zhou, and Alexei A. Efros. "Image-to-image translation with conditional adversarial networks." CVPR 2017.
- 49.
- 50. bit.ly/mmm-docxavi @DocXavi 50 Neural Machine Translation(NMT) Kyunghyun Cho, “Introduction to Neural Machine Translation with GPUs” (2015)
- 51.
- 52. bit.ly/mmm-docxavi @DocXavi 52 Automatic Speech Recognition(ASR) Slide: Hannun, Awni. "Sequence Modeling with CTC." Distill 2.11 (2017): e8.
- 53.
- 54. bit.ly/mmm-docxavi @DocXavi 54 Speech Synthesis Oord, Aaronvan den, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. "Wavenet: A generative model for raw audio." arXiv preprint arXiv:1609.03499 (2016).
- 55. bit.ly/mmm-docxavi @DocXavi 55 Encoder Decoder Representation Salvador, Amaia,Michal Drozdzal, Xavier Giro-i-Nieto, and Adriana Romero. "Inverse Cooking: Recipe Generation from Food Images." CVPR 2019.
- 56. bit.ly/mmm-docxavi @DocXavi 56 Image-to-Text Salvador, Amaia, MichalDrozdzal, Xavier Giro-i-Nieto, and Adriana Romero. "Inverse Cooking: Recipe Generation from Food Images." CVPR 2019.
- 57. bit.ly/mmm-docxavi @DocXavi 57 Chung, Joon Son,Andrew Senior, Oriol Vinyals, and Andrew Zisserman. "Lip reading sentences in the wild." CVPR 2017
- 58.
- 59. 59 #StackGAN Zhang, Han,Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas. "Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks." ICCV 2017. [code] Image Synthesis
- 60.
- 61. bit.ly/mmm-docxavi @DocXavi 61 Owens, Andrew, PhillipIsola, Josh McDermott, Antonio Torralba, Edward H. Adelson, and William T. Freeman. "Visually indicated sounds." CVPR 2016.
- 62.
- 63. bit.ly/DLCV2018 #DLUPC 63 Ephrat, Ariel, TaviHalperin, and Shmuel Peleg. "Improved speech reconstruction from silent video." In ICCV Workshop on Computer Vision for Audio-Visual Media. 2017.
- 64.
- 65. bit.ly/mmm-docxavi @DocXavi 65 Speech to Pixels AmandaDuarte, Francisco Roldan, Miquel Tubau, Janna Escur, Santiago Pascual, Amaia Salvador, Eva Mohedano et al. “Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks”. ICASSP 2019.
- 66.
- 67. bit.ly/mmm-docxavi @DocXavi 67 Multimodal Retrieval Amanda Duarte,Dídac Surís, Amaia Salvador, Jordi Torres, and Xavier Giró-i-Nieto. "Cross-modal Embeddings for Video and Audio Retrieval." ECCV Women in Computer Vision Workshop 2018. Best match Audio feature
- 68. bit.ly/mmm-docxavi @DocXavi 68 Multimodal Retrieval Best match Visual featureAudio feature Amanda Duarte, Dídac Surís, Amaia Salvador, Jordi Torres, and Xavier Giró-i-Nieto. "Cross-modal Embeddings for Video and Audio Retrieval." ECCV Women in Computer Vision Workshop 2018.
- 69.
- 70. bit.ly/mmm-docxavi @DocXavi 70 Harwath, David, AdriàRecasens, Dídac Surís, Galen Chuang, Antonio Torralba, and James Glass. "Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input." ECCV 2018
- 71.
- 72. bit.ly/mmm-docxavi @DocXavi 72 Visual Question Answering Antol,Stanislaw, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. "VQA: Visual question answering." CVPR 2015.
- 73.
- 74. bit.ly/DLCV2018 #DLUPC 74 Afouras, Triantafyllos, JoonSon Chung, and Andrew Zisserman. "The Conversation: Deep Audio-Visual Speech Enhancement." Interspeech 2018..
- 75.
- 76. bit.ly/DLCV2018 #DLUPC 76 Karras, Tero, TimoAila, Samuli Laine, Antti Herva, and Jaakko Lehtinen. "Audio-driven facial animation by joint end-to-end learning of pose and emotion." SIGGRAPH 2017
- 77.
- 78. bit.ly/commsenselab @DocXavi 78 Mnih, Volodymyr, KorayKavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. "Playing atari with deep reinforcement learning." NIPS Deep Learning Workshop (2013).
- 79. bit.ly/commsenselab @DocXavi 79 Beyond Multimedia #AlphaGo Silver,David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser et al. "Mastering the game of Go with deep neural networks and tree search." Nature 2016.
- 80. Wayve, “Sim2Real: Learningto Drive from Simulation without Real World Labels” (2018)
- 81. bit.ly/commsenselab @DocXavi Sermanet, Pierre, CoreyLynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain. "Time-contrastive networks: Self-supervised learning from video." ICRA 2018.
- 82. bit.ly/commsenselab @DocXavi 82 Beyond Multimedia Carl Ekin,Sims Whitherspoon, “Machine learning can boost the value of wind energy”. DeepMind (2019)
- 83. bit.ly/commsenselab @DocXavi 83 Beyond Multimedia 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.
- 84. bit.ly/commsenselab @DocXavi 84 Beyond Multimedia #AlphaFold Evans,R., J. Jumper, J. Kirkpatrick, L. Sifre, T. F. G. Green, C. Qin, A. Zidek et al. "De novo structure prediction with deeplearning based scoring." Annu Rev Biochem 77 (2018): 363-382. [blog]
- 85.
- 86. bit.ly/commsenselab @DocXavi 86 The AI Hype? Jitendra Malik, “What lead computer vision to deep learning ?” ACM Communications 2017.
- 87.
- 88. bit.ly/commsenselab @DocXavi 88 The AI Hype(?) Annual Conference on Neural Information Processing Systems (NIPS) @ Barcelona (2016)
- 89. bit.ly/commsenselab @DocXavi 89 The AI Hype(?) Annual Conference on Neural Information Processing Systems (NIPS) @ Long Beach (December 2017) - (Fig: Alex Lebrun)
- 90. bit.ly/commsenselab @DocXavi 90 The AI Hype(?) Annual Conference on Neural Information Processing Systems (NIPS) @ Montreal (December 2018)
- 91.
- 92. bit.ly/commsenselab @DocXavi 92 The AI Hype(?) CVPR is the top conference in computer science (IN ALL COMPUTER SCIENCE !!) Again (IN ALL COMPUTER SCIENCE !!) ...and Top #2 & #4 are on machine learning ...as Top #3 & #5 are on computer vision. Source: David Forsyth @ Good Citizen CVPR 2018 [video] [slides]
- 93.
- 94.
- 95. bit.ly/commsenselab @DocXavi 95 The AI Hype(?) Nature: Junior AI researchers are in demand by universities and industry (April 2019)
- 96. bit.ly/commsenselab @DocXavi 96 The AI Hype(?) 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
- 97. bit.ly/commsenselab @DocXavi 97 The AI Hype(?) Barack Obama, Neural Nets, Self-driving cars, and the Future of the World (Wired, June 2016)
- 98.
- 99.
- 100.
- 101. bit.ly/commsenselab @DocXavi 101 The AI Hype(?) LLegiu l’informe des d’aquí
- 102.
- 103.
- 104.
- 105.
- 106. bit.ly/commsenselab @DocXavi 106 Deep Learning courses@ UPC TelecomBCN: ● MSc course [2017] [2018] [2019] ● BSc course [2018] [2019] ● 1st edition (2016) ● 2nd edition (2017) ● 3rd edition (2018) ● 4th edition (2019) ● 1st edition (2017) ● 2nd edition (2018) ● 3rd edition - NLP (2019) Next edition: Autumn 2019 Included in ETSETB Master MATT Deep Learning Track + MET
- 107. bit.ly/commsenselab @DocXavi 107 Deep Learning forProfessionals @ UPC School Next edition starts November 2019. Sign up here.
- 108.
- 109. bit.ly/commsenselab @DocXavi 109 Our team fromUPC & BSC Barcelona Victor Campos Amaia Salvador Amanda Duarte Dèlia Fernández Eduard Ramon Andreu Girbau Dani Fojo Oscar Mañas Santi Pascual Xavi Giró Miriam Bellver Janna Escur Carles Ventura Paula Gómez Benet Oriol Mariona Carós Jordi Torres Ferran Marqués