Results 1 to 10 of about 57,048 (160)
Beyond ℓ1 sparse coding in V1. [PDF]
PLoS Computational Biology, 2023 Growing evidence indicates that only a sparse subset from a pool of sensory neurons is active for the encoding of visual stimuli at any instant in time.
Ilias Rentzeperis +3 more
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Fast Approximation for Sparse Coding with Applications to Object Recognition [PDF]
Sensors, 2021 Sparse Coding (SC) has been widely studied and shown its superiority in the fields of signal processing, statistics, and machine learning. However, due to the high computational cost of the optimization algorithms required to compute the sparse feature ...
Zhenzhen Sun, Yuanlong Yu
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Structural Smoothing Low-Rank Matrix Restoration Based on Sparse Coding and Dual-Weighted Model [PDF]
Entropy, 2022 Group sparse coding (GSC) uses the non-local similarity of images as constraints, which can fully exploit the structure and group sparse features of images.
Jiawei Wu, Hengyou Wang
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Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding [PDF]
Micromachines, 2023 To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention.
Yueran Qi +9 more
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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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Simultaneous Patch-Group Sparse Coding with Dual-Weighted ℓp Minimization for Image Restoration [PDF]
Micromachines, 2021 Sparse coding (SC) models have been proven as powerful tools applied in image restoration tasks, such as patch sparse coding (PSC) and group sparse coding (GSC). However, these two kinds of SC models have their respective drawbacks. PSC tends to generate
Jiachao Zhang, Ying Tong, Liangbao Jiao
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Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks [PDF]
Frontiers in Computational Neuroscience, 2020 Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation.
Xingyu Liu, Zonglei Zhen, Jia Liu
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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 ...
Yaniv Romano +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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