Deep Learning Summit (DLS01-5)
Deep learning is having a profound impact on AI applications. With the future of neural network-inspired computing in mind, re:Invent is hosting the first ever Deep Learning Summit. Designed for developers to learn about the latest in deep learning research and emerging trends, attendees will hear from industry thought leaders—members of the academic and venture capital communities—who will share their perspectives in 30-minute Lightning Talks. The Summit will be held on Thursday, November 30th at the Venetian from 1-5pm. The Deep Learning Revolution - Terrence Sejnowski, The Salk Institute for Biological Studies Eye, Robot: Computer Vision and Autonomous Robotics - Aaron Ames & Pietro Perona, California Institute of Technology Exploiting the Power of Language - Alexander Smola, Amazon Web Services Reducing Supervision: Making More with Less - Martial Herbert, Carnegie Mellon University Learning Where to Look in Video - Kristen Grauman, University of Texas Look, Listen, Learn: The Intersection of Vision and Sound - Antonio Torralba, MIT Investing in the Deep Learning Future - Matt Ocko, Data Collective Venture Capital
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Deep Learning Summit (DLS01-5)
- 1. Reducing Supervision: Making More with Less Martial Hebert, Carnegie Mellon University
- 11. Model dynamics
- 13. 𝜃 𝐿𝑖𝑣𝑖𝑛𝑔𝑅𝑜𝑜𝑚 Dual: Model space
- 14. 𝜃 𝐿𝑖𝑣𝑖𝑛𝑔𝑅𝑜𝑜𝑚 Dual: Model space
- 15. 𝜃 𝐿𝑖𝑣𝑖𝑛𝑔𝑅𝑜𝑜𝑚 𝜃𝑆𝑘𝑎𝑡𝑒𝑃𝑎𝑟𝑘 Dual: Model space
- 16. 𝜃 𝐿𝑖𝑣𝑖𝑛𝑔𝑅𝑜𝑜𝑚 𝜃𝑆𝑘𝑎𝑡𝑒𝑃𝑎𝑟𝑘 Dual: Model space
- 17. 𝜃 𝐿𝑖𝑣𝑖𝑛𝑔𝑅𝑜𝑜𝑚 𝜃𝑆𝑘𝑎𝑡𝑒𝑃𝑎𝑟𝑘 Dual: Model space
- 18. Model dynamics Learning to learn from small samples
- 19. 𝜃 𝑑𝑜𝑔 ∗ 𝜃 𝑑𝑜𝑔 𝑜
- 21. Res1 q1 0 Res2 Res3 Res4 Res5 q2 0 q4 0 q8 0 q16 0 q*
- 22. Type Method Accuracy (%) Plain ResNet152 48.03 Balanced-Data Over-Sampling 52.61 Down-Sampling 51.72 Balanced-Loss Cost-Sensitive 52.37 Ours MetaModelNet 57.34 He et al. 2016. Shen et al. 2016. Huang et al. 2016
- 23. Model dynamics Learning to learn from small samples Domain envelope
- 24. 𝜃 𝐿𝑖𝑣𝑖𝑛𝑔𝑅𝑜𝑜𝑚 0 50 100 150 200 250 300 350 400 Category index 0 200 400 600 800 1000 1200 #Occurrences
- 26. Model dynamics Learning to learn from small samples Domain envelope Composition
- 27. Possum
- 32. Possum Wet Dog
- 36. Attributes Big Red Old Images for Red Traditional vision system
- 37. Attributes Objects ElephantWine TomatoTV Dog Big Red Old Traditional vision system
- 38. Attribute s Objects ElephantWine TomatoTV Dog Big Red Old Images for Tomato Traditional vision system
- 40. Big Attributes Objects Red Old ElephantWine TomatoTV Dog Red Tomato Images for Red Tomato Traditional vision system
- 41. Big Attributes Objects Red Old ElephantWine TomatoTV Dog Red Tomato Separate classifier for red, tomato and red tomato Traditional vision system
- 42. Attributes Objects ElephantWine TomatoTV Dog Big Red Old Red Wine Separate classifier for red, wine and red wine Red Tomato Traditional vision system
- 43. Attributes Objects ElephantWine TomatoTV Dog Big Red Old Combinations Separate classifiers for objects, attributes, and pairs Traditional vision system
- 44. Attributes Objects ElephantWine TomatoTV Dog Big Red Old Combinations Traditional system problem: Lots of labels
- 45. Cannot compose existing concepts Cannot generalize well possum wet wet possum + = Traditional system problem: Generalize
- 48. Wet Dog Wet dog
- 49. Transform Network Compositionality Transform D Wine Classifier Weights LeakyReLU LeakyReLU Red D Red Wine Input Concepts Model regression – Wang & Hebert, 2016
- 50. Red Wine Classifier Weights Red Wine Imag Label Red Wine ConvNet Dot Produc t Loss Transform Network
- 51. Train on few combinations Classifier Weights Combination Classifier Wet Dog Wet Dog Transform NetworkWet Attributes Objects Red Old PossumWine TomatoTV Dog Combinations Train
- 52. Test on unseen combinations Wet Attributes Objects Red Old PossumWine TomatoTV Dog Combinations Train Classifier Weights Combination Classifier Wet Possum Wet Possum Transform Network Test
- 53. Retrievals on unseen pairs Cracked egg Peeled egg New truck Rusted truck Broken phone Broken glasses
- 54. Moving Beyond Two Inputs: Subject Predicate Objects Subject: man Object: bicycle Different predicates Extension of standard SVO tasks – Sadeghi & Farhadi, 2011 riding falling pushing carrying
- 55. Model dynamics Learning to learn from small samples Domain envelope Composition Simulation
- 56. Synthetic world Real world
- 58. Model dynamics Learning to learn from small samples Domain envelope Composition Simulation Evolving the network structure
- 59. Task I Task II
- 60. Evolving the Model Structure Task I Task II
- 61. Novel task image Novel task ground truth Novel task image Novel task ground truth Novel task image Novel task ground truth Novel task image Novel task ground truth Novel task image Novel task ground truth Augmented Pre-trained Classifier Pre-Trained Network Depth Augmented Network Jointly Depth and Width Augmented Network Width Augmented Network Recursively Width Augmented Network Conventional Fine-Tuning fc6 fc7 fca
- 62. Fine-tuning Our approach Consistent improvement across classification benchmarks
- 63. AlexNet WideAlexNet Ours (progressive widening) But wait... is this simple stating that deeper (or wider) networks are better?