22,250 research outputs found
Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop
edge-like receptive fields. One approach to understanding the emergence of this
response is to state that neural activity has to efficiently represent sensory
data with respect to the statistics of natural scenes. Furthermore, it is
believed that such an efficient coding is achieved using a competition across
neurons so as to generate a sparse representation, that is, where a relatively
small number of neurons are simultaneously active. Indeed, different models of
sparse coding, coupled with Hebbian learning and homeostasis, have been
proposed that successfully match the observed emergent response. However, the
specific role of homeostasis in learning such sparse representations is still
largely unknown. By quantitatively assessing the efficiency of the neural
representation during learning, we derive a cooperative homeostasis mechanism
that optimally tunes the competition between neurons within the sparse coding
algorithm. We apply this homeostasis while learning small patches taken from
natural images and compare its efficiency with state-of-the-art algorithms.
Results show that while different sparse coding algorithms give similar coding
results, the homeostasis provides an optimal balance for the representation of
natural images within the population of neurons. Competition in sparse coding
is optimized when it is fair. By contributing to optimizing statistical
competition across neurons, homeostasis is crucial in providing a more
efficient solution to the emergence of independent components
A New Approach to Sparse Image Representation Using MMV and K-SVD
This paper addresses the problem of image representation based on a sparse decomposition over a learned dictionary. We propose an improved matching pursuit algorithm for Multiple Measurement Vectors (MMV) and an adaptive algorithm for dictionary learning based on multi-Singular Value Decomposition (SVD), and combine them for image representation. Compared with the traditional K-SVD and orthogonal matching pursuit MMV (OMPMMV) methods, the proposed method runs faster and achieves a higher overall reconstruction accuracy
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