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P01 introduction cvpr2012 deep learning methods for vision
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P01 introduction cvpr2012 deep learning methods for vision

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  • All I am going to say about Neuroscience, although techniques do have strong connections.
  • Make clear that classic methods, e.g.convnets are purely supervised.
  • Need to bring outdiffereceswrt to existing ML stuff, mainly unsupervised learning part. Make use of unlabaled data (lots of it).
  • Restructure to bigger emphasis on unsupervised.Make clear that classic methods, e.g.convnets are purely supervised.
  • Winder and Brown paper. Slightly smoothed view of things.
  • Selection instead of normalization?
  • Note pooling is across space, not across Gabor channelNormalization is really nonlinear (small elements not rescaled)
  • Non-maximal suppression across VW. Like an L-InfnormalizationMax = k-means
  • Graph not clear. Explain better. Y-axis is change in value
  • Mention Leonardis & Fidler paper
  • Too far for labels to trickle down (vanishing gradients)Only information from layer below.Input is supervision.
  • Add overall energy
  • Not separate operations Do it at the same
  • Chriswilliams oral link
  • Occlusion mask: bootom right quad for sofa interpretationCan’t decide locally If you knew solution, would know what features to extract.
  • DPM is shape hierarchical HOG templates
  • DPM is shape hierarchical HOG templates
  • Song Chun ‘s clock
  • Transcript

    • 1. Deep Learning &Feature LearningMethods for Vision ’
    • 2. Tutorial Overview
    • 3. Overview• –• – – –•
    • 4. Existing Recognition Approach••
    • 5. Motivation•• –•
    • 6. What Limits Current Performance?• –•
    • 7. Hand-Crafted Features• β –• –•
    • 8. Mid-Level Representations•“ ”•• 
    • 9. Why Learn Features?•• – – –• – –
    • 10. Why Hierarchy? fi
    • 11. Hierarchies in Vision• –• –
    • 12. Hierarchies in Vision••
    • 13. Learning a Hierarchy of Feature Extractors•• ••
    • 14. Multistage Hubel-Wiesel Architecture•••••••
    • 15. Classic Approach to Training• – – –• – –
    • 16. Deep Learning••••
    • 17. Single Layer Architecture
    • 18. Example Feature Learning Architectures
    • 19. SIFT Descriptor
    • 20. Spatial Pyramid Matching
    • 21. Filtering• –
    • 22. Filtering• – –  . . .
    • 23. Translation Equivariance•  – –
    • 24. Filtering• – –
    • 25. Filtering• – – – –
    • 26. Normalization• •
    • 27. Normalization• –  –
    • 28. Normalization• – –
    • 29. Role of Normalization• – “ ” – –• |.|1 |.|1 |.|1 |.|1
    • 30. Pooling• – – –
    • 31. Role of Pooling• – –
    • 32. Role of Pooling• • • •
    • 33. Unsupervised Learning••• – – –
    • 34. Auto-Encoder
    • 35. Auto-Encoder Example 1• σ(WTz) σ(Wx) σ σ
    • 36. Auto-Encoder Example 2• Dz σ(Wx) σ
    • 37. Auto-Encoder Example 2• Dz σ(Wx) σ
    • 38. Taxonomy of Approaches• – – –• – –• –
    • 39. Stacked Auto-Encoders
    • 40. At Test Time••••
    • 41. Information Flow in Vision Models•• – –• –
    • 42. Deep Boltzmann Machines
    • 43. Why is Top-Down important?•••
    • 44. Multi-Scale Models• • • HOG Pyramid
    • 45. Hierarchical Model• Input Image/ Features Input Image/ Features
    • 46. Multi-scale vs Hierarchical Feature Pyramid Input Image/ Features
    • 47. Structure Spectrum• – – –• – –
    • 48. Structure Spectrum• – –
    • 49. Structure Spectrum••
    • 50. Structure Spectrum• – –
    • 51. Structure Spectrum•••
    • 52. Structure Spectrum• – –
    • 53. Structure Spectrum• –• –•
    • 54. Structure Spectrum• – – –
    • 55. Structure Spectrum• – – –
    • 56. Performance of Deep Learning•• – • – •• –• –•
    • 57. Summary• –•••
    • 58. Further Resources•••• –•
    • 59. References••••••• ••••
    • 60. References••••••••
    • 61. References••••••••
    • 62. References•••••••••
    • 63. References•••••••
    • 64. References••••••••••
    • 65. References•••••••••
    • 66. References••••