1. “When” rather than “Whether”: Developmental Variable Selection Melissa Dominguez Robert Jacobs Department of Computer Science University of Rochester

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. 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. 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. Less is More and binocular disparity detection A richly detailed pair of pictures The same pair of pictures, blurred

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. Architecture

8. Left and Right Images 1 dimensional images –Horizontal and vertical disparities exist –Only horizontal mean depth Left Right

9. Binocular Energy Filters Make comparisons in the energy domain Based on neurophysiology Compute Gabor functions of left and right eye images

10. Adaptable Portion

11. All input at once Unstaged Model

12. Progressive models Developmental Model Inverse Developmental Model Input in stages during training

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. Data Solid Object Noisy Object Planar Stereogram

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. Solid Object Learning Curve

17. Solid Object Results

18. Noisy Object Results

19. Planar Stereogram Results

20. Results (t-values)

21. Result summary Overall Developmental and Inverse Developmental models performed best Random and Unstaged models performed worst

22. Why does Developmental model have high variance?

23. 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

24. –Prediction: F-CF-CMF or C-CF-CMF perform poorly Suitably designed developmental sequences can aid learning of complex vision tasks Development Aids Learning

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