Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Robert Jacobs, Melissa Dominguez
We consider the hypothesis that systems learning aspects of visual per- ception may benefit from the use of suitably designed developmental pro- gressions during training. Four models were trained to estimate motion velocities in sequences of visual images. Three of the models were “de- velopmental models” in the sense that the nature of their input changed during the course of training. They 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 develop- mental progression (i.e. 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 non- developmental 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. We conclude that suit- ably designed developmental sequences can be useful to systems learn- ing to estimate motion velocities. The idea that visual development can aid visual learning is a viable hypothesis in need of further study.