Learning Stereo Features with Stacked Autoencoders
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چکیده
Single-layer stacked autoencoders have been shown to be successful in training artificial neurons with receptive fields that are similar to those found in the V1 cortex, but on monocular data. In this project we investigate extending a single-layer stacked autoencoder network to learn receptive fields on stereo data, and evaluate them with respect to their effectiveness as features for object classification and their similarity to neuronal receptive fields characterized in physiological experiments.
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تاریخ انتشار 2009