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Learning to combine foveal glimpses with a third-order Boltzmann machine
This video was recorded at 24th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2010. We describe a model based on a Boltzmann machine with third-order connections that can learn how to accumulate information about a shape over several fixations. The model uses a retina that only has enough high resolution pixels to cover a small area of the image, so it must decide on a sequence of fixations and it must combine the "glimpse" at each fixation with the location of the fixation before integrating the information with information from other glimpses of the same object. We evaluate this model on a synthetic dataset and two image classification datasets, showing that it can perform at least as well as a model trained on whole images.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
