Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images
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Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images
The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a...
متن کاملSparse Codes for Natural Images
The human visual system, at the primary cortex, has receptive fields that are spatially localized, oriented and bandpass. It has been shown that a certain learning algorithm to produce sparse codes for natural images leads to basis functions with similar properties. This learning algorithm optimizes a cost function that trades off representation quality for sparseness, and searches for sets of ...
متن کامل1 Sparse Codes for Natural Images
The human visual system, at the primary cortex, has receptive fields that are spatially localized, oriented and bandpass. It has been shown that a certain learning algorithm to produce sparse codes for natural images leads to basis functions with similar properties. This learning algorithm optimizes a cost function that trades off representation quality for sparseness, and searches for sets of ...
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Images can be coded accurately using a sparse set of vectors from a learned overcomplete dictionary, with potential applications in image compression and feature selection for pattern recognition. We present a survey of algorithms that perform dictionary learning and sparse coding and make three contributions. First, we compare our overcomplete dictionary learning algorithm (FOCUSS-CNDL) with o...
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We show how the principle of sparse coding may be applied to learn the forms of structure occurring in time-varying natural images. A sequence of images is described as a linear superposition of space-time functions , each of which is convolved with a time-varying coeecient signal. When a sparse, independent representation is sought over the coeecients, the basis functions that emerge are space...
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ژورنال
عنوان ژورنال: PLoS Computational Biology
سال: 2013
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1003182