Learning Multiple Layers of Features from Tiny Images

@inproceedings{Krizhevsky2009LearningML,
  title={Learning Multiple Layers of Features from Tiny Images},
  author={Alex Krizhevsky},
  year={2009}
}
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 training of deep generative models, but previous researchers who have tried this have found it dicult to learn a good set of lters from the images. We show how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex. Using a novel parallelization algorithm to… CONTINUE READING
Highly Influential
This paper has highly influenced 989 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 5,169 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 3,270 extracted citations

A General Framework for Linear Distance Preserving Hashing

IEEE Transactions on Image Processing • 2018
View 9 Excerpts
Highly Influenced

A Unified Approximation Framework for Deep Neural Networks

Yuzhe Ma, Ran Chen, +4 authors Bei Yu
ArXiv • 2018
View 5 Excerpts
Highly Influenced

A Walk with SGD

View 16 Excerpts
Highly Influenced

5,169 Citations

010002000'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 5,169 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…