P01 introduction cvpr2012 deep learning methods for vision

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Transcript of P01 introduction cvpr2012 deep learning methods for vision

  • 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 8. Mid-Level Representations 9. Why Learn Features? 10. Why Hierarchy? 11. Hierarchies in Vision 12. Hierarchies in Vision 13. Learning a Hierarchyof 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(WTz)(Wx) 36. Auto-Encoder Example 2Dz(Wx) 37. Auto-Encoder Example 2Dz(Wx) 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 ModelsHOG Pyramid 45. Hierarchical ModelInput Image/ FeaturesInput Image/ Features 46. Multi-scalevs Hierarchical Feature PyramidInput 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