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NECO
2007

Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning

11 years 5 months ago
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki and Sejnowski, 2000; Teh et al., 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback and lateral connections, which is trained to approximate the SWM. Learning is done with a variant ...
Joseph F. Murray, Kenneth Kreutz-Delgado
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where NECO
Authors Joseph F. Murray, Kenneth Kreutz-Delgado
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