Neural networks and the bias/variance dilemma
Neural Computation
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A statistical approach to decision tree modeling
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
| Hi-index | 0.00 |
We consider the hypothesis that systems learning aspects of visual perception may benefit from the use of suitably designed developmental progressions during training. Four models were trained to estimate motion velocities in sequences of visual images. Three of the models were developmental models in the sense that the nature of their visual input changed during the course of training. These models received a relatively impoverished visual input early in training, and the quality of this input improved as training progressed. One model used a coarse-to-multiscale developmental progression (it received coarse-scale motion features early in training and finer-scale features were added to its input as training progressed), another model used a fine-to-multiscale progression, and the third model used a random progression. The final model was nondevelopmental in the sense that the nature of its input remained the same throughout the training period. The simulation results show that the coarse-to-multiscale model performed best. Hypotheses are offered to account for this model's superior performance, and simulation results evaluating these hypotheses are reported. We conclude that suitably designed developmental sequences can be useful to systems learning to estimate motion velocities. The idea that visual development can aid visual learning is a viable hypothesis in need of further study.