“when” rather than “whether”: developmental variable selection

“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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1.2

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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

cfm

Conclusions

• Performance of a system can be improved by judiciously choosing when to include each variable– Randomly staggering variables is not enough