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“When” rather than “Whether”:Developmental Variable
Selection
Melissa Dominguez
Robert Jacobs
Department of Computer Science
University of Rochester
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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
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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
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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
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Less is More and binocular disparity detection
A richly detailed pair of pictures
The same pair of pictures, blurred
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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
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Architecture
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Left and Right Images
• 1 dimensional images– Horizontal and vertical disparities exist– Only horizontal mean depth
LeftRight
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Binocular Energy Filters
• Make comparisons in the energy domain
• Based on neurophysiology
• Compute Gabor functions of left and right eye images
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Adaptable Portion
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• All input at once
Unstaged Model
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Progressive models
Developmental Model Inverse Developmental Model
• Input in stages during training
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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
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Data
Solid Object
Noisy Object
Planar Stereogram
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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
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Solid Object Results
Solid Object Results
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1.2
1.4
developmental
inverse devleopmental
unstaged
randomized
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Noisy Object Results
Noisy Object Results
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0.2
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0.6
0.8
1
1.2
1.4
developmental
inverse developmental
unstaged
randomized
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Planar Stereogram Results
Planar Stereogram Results
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developmental
inverse developmental
unstaged
randomized
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Result summary
• Overall Developmental and Inverse Developmental models performed best
• Random and Unstaged models performed worst
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• 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
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– 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
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1
1.2
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1.6
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developmental
inverse developmental
unstaged
randomized
fcm
cfm
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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