Results 11 to 20 of about 223,248 (273)
Joint bayesian convolutional sparse coding for image super-resolution. [PDF]
PLoS ONE, 2018 We propose a convolutional sparse coding (CSC) for super resolution (CSC-SR) algorithm with a joint Bayesian learning strategy. Due to the unknown parameters in solving CSC-SR, the performance of the algorithm depends on the choice of the parameter.
Qi Ge, Wenze Shao, Liqian Wang
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Quantum Machine Intelligence, 2022 Abstract The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is NP-hard in general, and it is therefore always approached with an approximation method, such as lasso or orthogonal matching pursuit, thus trading off accuracy ...
Romano, Yaniv +7 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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Foundations and Trends® in Communications and Information Theory, 2019 Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. These
Venkataramanan, Ramji +2 more
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A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval. [PDF]
PLoS ONE, 2015 Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive
Yunchao Zhang +3 more
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