Deep Learning &Feature LearningMethods for Vision    ’
Tutorial Overview
Overview•    –•    –    –    –•
Existing Recognition Approach••
Motivation••    –•
What Limits Current Performance?•    –•
Hand-Crafted Features•       β    –•    –•
Mid-Level Representations•“   ”••                   
Why Learn Features?••    –    –    –•    –    –
Why Hierarchy?     fi
Hierarchies in Vision•    –•    –
Hierarchies in Vision••
Learning a Hierarchy    of Feature Extractors••           ••
Multistage Hubel-Wiesel Architecture•••••••
Classic Approach to Training•    –    –    –•    –    –
Deep Learning••••
Single Layer Architecture
Example Feature Learning Architectures
SIFT Descriptor
Spatial Pyramid Matching
Filtering•    –
Filtering•    –    –                        .                .                .
Translation Equivariance•                  –    –
Filtering•    –    –
Filtering•    –    –    –    –
Normalization•    •
Normalization•    –                       –
Normalization•    –    –
Role of Normalization•    – “      ”    –    –•                       |.|1   |.|1   |.|1   |.|1
Pooling•    –    –    –
Role of Pooling•    –    –
Role of Pooling•    •    •    •
Unsupervised Learning•••    –    –    –
Auto-Encoder
Auto-Encoder Example 1•          σ(WTz)      σ(Wx)    σ                            σ
Auto-Encoder Example 2•        Dz        σ(Wx)                             σ
Auto-Encoder Example 2•        Dz        σ(Wx)                             σ
Taxonomy of Approaches•    –    –    –•    –    –•    –
Stacked Auto-Encoders
At Test Time••••
Information Flow in Vision Models••    –    –•    –
Deep Boltzmann Machines
Why is Top-Down important?•••
Multi-Scale Models•    •    •        HOG Pyramid
Hierarchical Model•    Input Image/ Features    Input Image/ Features
Multi-scale        vs   Hierarchical Feature Pyramid          Input Image/ Features
Structure Spectrum•    –    –    –•    –    –
Structure Spectrum•    –    –
Structure Spectrum••
Structure Spectrum•    –    –
Structure Spectrum•••
Structure Spectrum•    –    –
Structure Spectrum•    –•    –•
Structure Spectrum•    –    –    –
Structure Spectrum•    –    –    –
Performance of Deep Learning••    –        •    –        ••    –•    –•
Summary•    –•••
Further Resources••••    –•
References••••••• ••••
References••••••••
References••••••••
References•••••••••
References•••••••
References••••••••••
References•••••••••
References••••
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
  • P01 introduction cvpr2012 deep learning methods for vision

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

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