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“When” rather than “Whether”:Developmental Variable
Selection
Melissa Dominguez
Robert Jacobs
Department of Computer Science
University of Rochester
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
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
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
Less is More and binocular disparity detection
A richly detailed pair of pictures
The same pair of pictures, blurred
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
Architecture
Left and Right Images
• 1 dimensional images– Horizontal and vertical disparities exist– Only horizontal mean depth
LeftRight
Binocular Energy Filters
• Make comparisons in the energy domain
• Based on neurophysiology
• Compute Gabor functions of left and right eye images
Adaptable Portion
• All input at once
Unstaged Model
Progressive models
Developmental Model Inverse Developmental Model
• Input in stages during training
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
Data
Solid Object
Noisy Object
Planar Stereogram
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
Solid Object Results
Solid Object Results
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developmental
inverse devleopmental
unstaged
randomized
Noisy Object Results
Noisy Object Results
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developmental
inverse developmental
unstaged
randomized
Planar Stereogram Results
Planar Stereogram Results
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developmental
inverse developmental
unstaged
randomized
Result summary
• Overall Developmental and Inverse Developmental models performed best
• Random and Unstaged models performed worst
• 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
– 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
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developmental
inverse developmental
unstaged
randomized
fcm
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Conclusions
• Performance of a system can be improved by judiciously choosing when to include each variable– Randomly staggering variables is not enough