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Sparse Coding and Autoencoders [PDF]
2018 IEEE International Symposium on Information Theory (ISIT), 2018 In this new version of the paper with a small change in the distributional assumptions we are actually able to prove the asymptotic criticality of a neighbourhood of the ground truth dictionary for even just the standard squared loss of the ReLU autoencoder (unlike the regularized loss in the older version)
Akshay Rangamani +6 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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Sparse representations in audio & music: from coding to source separation [PDF]
, 2010 —Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME +15 more
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Frontiers in Computational Neuroscience, 2019 Sparse coding models of natural images and sounds have been able to predict several response properties of neurons in the visual and auditory systems. While the success of these models suggests that the structure they capture is universal across domains ...
Eric McVoy Dodds +4 more
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IEEE Transactions on Information Theory, 2014 51 pages, 12 figures, submitted to IEEE Transactions on Information ...
Shenghao Yang 0001, Raymond W. Yeung
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Sparse representation based intraframe and semi‐intraframe video coding schemes for low bitrates
IET Image Processing, 2021 This paper proposes some extensions of the successful sparse coding of still images to intraframe and semi‐intraframe video coding. The presented frameworks apply the efficient K‐singular value decomposition and recursive least squares dictionary ...
Maziar Irannejad +1 more
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Disaggregating Transform Learning for Non-Intrusive Load Monitoring
IEEE Access, 2018 This paper addresses the problem of energy disaggregation/non-intrusive load monitoring. It introduces a new method based on the transform learning formulation. Several recent techniques, such as discriminative sparse coding, powerlet disaggregation, and
Megha Gaur, Angshul Majumdar
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Integrated Sparse Coding With Graph Learning for Robust Data Representation
IEEE Access, 2020 Sparse coding is a popular technique for achieving compact data representation and has been used in many applications. However, the instability issue often causes degeneration in practice and thus attracts a lot of studies.
Yupei Zhang, Shuhui Liu
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LOW-DIMENSIONAL STRUCTURES: SPARSE CODING FOR NEURONAL ACTIVITY [PDF]
Journal of Innovative Optical Health Sciences, 2013 Neuronal ensemble activity codes working memory. In this work, we developed a neuronal ensemble sparse coding method, which can effectively reduce the dimension of the neuronal activity and express neural coding.
YUNHUA XU, WENWEN BAI, XIN TIAN
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An Optimum Deraining Scheme using Sparse Coding
International Journal of Emerging Research in Engineering, Science, and Management, 2022 Rain streak removal is a challenging and interesting task of image processing where the rain streaks will be removed from an image with rain streaks. In the literature, a large number of proposals are made where rain streak removal is considered as image
A Hazarathaiah +4 more
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