Extracting and composing robust features with denoising autoencoders

@inproceedings{Vincent2008ExtractingAC,
  title={Extracting and composing robust features with denoising autoencoders},
  author={Pascal Vincent and Hugo Larochelle and Yoshua Bengio and Pierre-Antoine Manzagol},
  booktitle={ICML},
  year={2008}
}
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders… CONTINUE READING
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Y. Bengio
Université de Montréal, dept. IRO • 2007
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