Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. [PDF]
PLoS Computational Biology, 2013 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
Joel Zylberberg, Michael Robert DeWeese
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Semi-Supervised Sparse Coding [PDF]
2014 International Joint Conference on Neural Networks (IJCNN), 2015 Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised ...
Gao, Xin, Wang, Jim Jing-Yan
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Sparse Spectrotemporal Coding of Sounds [PDF]
EURASIP Journal on Advances in Signal Processing, 2003 Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals.
Körding Konrad P +2 more
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Adaptive sparse coding based on memristive neural network with applications. [PDF]
Cogn Neurodyn, 2019 Ji X +5 more
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Research on software credibility algorithm based on deep convolutional sparse coding [PDF]
MATEC Web of Conferences, 2021 Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the ...
Xu Zhaosheng
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Image Classification Based on Neighborhood Preserving Embedding Sparse Coding [PDF]
Jisuanji gongcheng, 2016 Aiming at the problem of image classification with a complex background,this paper proposes a new image algorithm based on Neighborhood Preserving Embedding regularization Sparse Coding algorithm(NPESC).Comparing with traditional sparse coding,it adds ...
GAO Jiaxue,CHEN Xiuhong
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Sparse-Coding Variational Autoencoders [PDF]
Neural Computation, 2018 Abstract The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations; however: (1) computing the neural response to an image patch required minimizing a nonlinear ...
Victor Geadah +4 more
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Image Classification Method Based on Non-negative Elastic Net Sparse Coding Algorithm [PDF]
Jisuanji gongcheng, 2017 In order to improve the image classification accuracy,this paper proposes a Non-negative Elastic Net Sparse Coding(NENSC)algorithm.This algorithm combines the advantages of non-negative sparse coding and elastic net algorithm.It introduces an l2norm ...
ZHANG Yong,ZHANG Yangyang,CHENG Hong,ZHANG Yanxia
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Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization
Applied Sciences, 2022 Sparse orthonormal transform is based on orthogonal sparse coding, which is relatively fast and suitable in image compression such as analytic transforms with better performance.
Gihwan Lee, Yoonsik Choe
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Efficient sparse coding in early sensory processing: lessons from signal recovery. [PDF]
PLoS Computational Biology, 2012 Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as ...
András Lörincz +2 more
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