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2.[PDF] Learning Multiple Layers of Features from Tiny Images

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  • Descriptions: It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, …
  • More : It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, …
  • Source : https://www.semanticscholar.org/paper/Learning-Multiple-Layers-of-Features-from-Tiny-Krizhevsky/5d90f06bb70a0a3dced62413346235c02b1aa086

3.Learning multiple layers of features from tiny images – BibBase

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  • Descriptions: Learning multiple layers of features from tiny images. Krizhevsky, A. & Hinton, G. Technical Report 0, University of Toronto, Toronto, Ontario, 2009.
  • More : Learning multiple layers of features from tiny images. Krizhevsky, A. & Hinton, G. Technical Report 0, University of Toronto, Toronto, Ontario, 2009.
  • Source : https://bibbase.org/network/publication/krizhevsky-hinton-learningmultiplelayersoffeaturesfromtinyimages-2009

4.Learning Multiple Layers of Features from Tiny Images – BibSonomy

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  • Descriptions: Learning Multiple Layers of Features from Tiny Images. A. Krizhevsky. (2009 ). Document: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf …
  • More : Learning Multiple Layers of Features from Tiny Images. A. Krizhevsky. (2009 ). Document: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf …
  • Source : https://www.bibsonomy.org/bibtex/cc2d42f2b7ef6a4e76e47d1a50c8cd86

5.Krizhevsky, A. (2009) Learning Multiple Layers of Features from Tiny …

  • Author: Krizhevsky,
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  • Descriptions: By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high …
  • More : By dividing image data into subbands, important feature learning occurred over differing low to high frequencies. The combination of the learned low and high …
  • Source : https://www.scirp.org/(S(czeh2tfqyw2orz553k1w0r45))/reference/ReferencesPapers.aspx%3FReferenceID%3D2208762

6.Learning Multiple Layers of Features from Tiny Images – Scinapse

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  • Descriptions: Learning Multiple Layers of Features from Tiny Images … In this work we describe how to train a multi-layer generative model of natural images. We use a dataset …
  • More : Learning Multiple Layers of Features from Tiny Images … In this work we describe how to train a multi-layer generative model of natural images. We use a dataset …
  • Source : https://www.scinapse.io/papers/3118608800

7.Learning Multiple Layers of Features from Tiny Images

  • Author: Learning
  • Publish: 12 days ago
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  • Descriptions: Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised …
  • More : Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. It is, in principle, an excellent dataset for unsupervised …
  • Source : https://learning2hash.github.io/publications/cifar2009learning/

8.bibtex/wqren.bib at master – GitHub

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  • Descriptions: title={Learning multiple layers of features from tiny images},. author={Krizhevsky, A. and Hinton, G.},. journal={Master’s thesis, Department of Computer …
  • More : title={Learning multiple layers of features from tiny images},. author={Krizhevsky, A. and Hinton, G.},. journal={Master’s thesis, Department of Computer …
  • Source : https://github.com/wqren/bibtex/blob/master/wqren.bib

9.Learning multiple layers of features from tiny images – 百度学术

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  • Descriptions: Learning multiple layers of features from tiny images · ResearchGate · Citeseer · Citeseer · documbase.com · core.ac.uk · mendeley.com · github.com (全网免费下载) …
  • More : Learning multiple layers of features from tiny images · ResearchGate · Citeseer · Citeseer · documbase.com · core.ac.uk · mendeley.com · github.com (全网免费下载) …
  • Source : http://a.xueshu.baidu.com/usercenter/paper/show%3Fpaperid%3Dc55665fb879e98e130fce77052d4c8e8

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