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Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article ...
Peter Földiák, Dominik M. Endres
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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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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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Sparse-Coding Variational Autoencoders. [PDF]
Neural Comput, 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 ...
Geadah V +4 more
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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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Fast Convolutional Sparse Coding [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e.
Hilton Bristow +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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