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  1. 1. “When” rather than “Whether”: Developmental Variable Selection Melissa Dominguez Robert Jacobs Department of Computer Science University of Rochester
  2. 2. Introduction • Using human developmental theories as an inspiration for machine learning – Don’t use all variables at once – Focus on choice of when to include certain variables • A system which uses this process to learn disparity sensitivities
  3. 3. Human Perceptual Development • Humans are born with limited sensory and cognitive abilities • Two main schools of thought about early limitations – Traditional view • Immaturities are barriers to be overcome – “Less is More” view • Early limitations are helpful
  4. 4. Less is More in vision • Newborns have poor visual acuity – Improves approx. linearly to near adult levels by about 8 months of age • Other visual skills are being acquired at the same time – Sensitivity to disparities around 4 months • We propose that early poor acuity helps in acquisition of disparity sensitivity
  5. 5. Less is More and binocular disparity detection A richly detailed pair of pictures The same pair of pictures, blurred
  6. 6. Previous coarse to fine approaches • Coarse to fine approaches – First search low resolution image pair – Then refine estimate with high resolution pair • Marr and Poggio, 1979; Quam, 1986; Barnard, 1987; Iocchi and Konolidge, 1998 • Previous approaches are processing strategies - not developmental sequences
  7. 7. Architecture
  8. 8. Left and Right Images • 1 dimensional images – Horizontal and vertical disparities exist – Only horizontal mean depth Left Right
  9. 9. Binocular Energy Filters • Make comparisons in the energy domain • Based on neurophysiology • Compute Gabor functions of left and right eye images
  10. 10. Adaptable Portion
  11. 11. • All input at once Unstaged Model
  12. 12. Progressive models Developmental Model Inverse Developmental Model • Input in stages during training
  13. 13. Random Model • Still have 3 stages – Stage 1 consists of a randomly selected third of the input units – In subsequent stages add another randomly selected third of the input units – Stages consist of same inputs across data items
  14. 14. Data Solid Object Noisy Object Planar Stereogram
  15. 15. Procedures • Conjugate gradient training procedure • 10 runs of each model for each data set – 35 iterations per run • Stages of 10, 10, and 15 iterations • Randomly generated training set • Test sets had evenly spaced disparities – Randomly generated object size and location
  16. 16. Solid Object Results Solid Object Results 0 0.2 0.4 0.6 0.8 1 1.2 1.4 developmental inverse devleopmental unstaged randomized
  17. 17. Noisy Object Results Noisy Object Results 0 0.2 0.4 0.6 0.8 1 1.2 1.4 developmental inverse developmental unstaged randomized
  18. 18. Planar Stereogram Results Planar Stereogram Results 0 0.2 0.4 0.6 0.8 1 1.2 1.4 developmental inverse developmental unstaged randomized
  19. 19. Result summary • Overall Developmental and Inverse Developmental models performed best • Random and Unstaged models performed worst
  20. 20. • Why do Developmental and Inverse Developmental models work best? – Limitations on initial input size? • NO! Random model results show otherwise – Hypothesis: • Important to combine features at same scale in early stages • Important to proceed to neighboring scales in stages
  21. 21. – Prediction: F-CF-CMF or C-CF-CMF perform poorly Suitably designed developmental sequences can aid learning of complex vision tasks Development Aids LearningDevelopment Aids Learning 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 developmental inverse developmental unstaged randomized fcm cfm
  22. 22. Conclusions • Performance of a system can be improved by judiciously choosing when to include each variable – Randomly staggering variables is not enough

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