Presentation 2007-03-16
Learning nonlinear forward optics in generative models
Satohiro TAJIMA, Masataka WATANABE,
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Abstract(in English) Visual processing is an inverse problem, say, the system needs to unravel the three-imensional representation of the world relying on two-dimensional retinal input. Although the generative model is a promising method, acquisition of complex forward optics remains a problem. Here we propose a solution to this problem using a multilayered neural network with backpropagation learning. Unlike conventional applications of multilayered network to vision, we assume that inputs are high-level object representations, whereas the desired output is low level retinal representation. We chose "occlusion" as an example nonlinear forward optics and simulated the learning process in accordance to the findings that infants learn to recognize occlusion at a later stage of visual development. We incorporated this network into a generative model and simulated the estimation process of object depth when given partially occluded multiple objects. Analysis of hidden units revealed that there exists various type of neural preference depending on the luminance of occluded and non-occluded surface of visual objects.
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Keyword(in English) Generative Model / Top-Down / Visual Processing / Occlusion / Back-Propagation
Paper # NC2006-188
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Conference Information
Committee NC
Conference Date 2007/3/9(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Learning nonlinear forward optics in generative models
Sub Title (in English)
Keyword(1) Generative Model
Keyword(2) Top-Down
Keyword(3) Visual Processing
Keyword(4) Occlusion
Keyword(5) Back-Propagation
1st Author's Name Satohiro TAJIMA
1st Author's Affiliation Faculty of Engineering, University of Tokyo()
2nd Author's Name Masataka WATANABE
2nd Author's Affiliation Faculty of Engineering, University of Tokyo
Date 2007-03-16
Paper # NC2006-188
Volume (vol) vol.106
Number (no) 590
Page pp.pp.-
#Pages 6
Date of Issue