Transcription of Extracting and Composing Robust Features with Denoising ...
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Extracting and Composing Robust Features with Denoising ...
Extracting and Composing Robust Features withDenoising AutoencodersPascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine ManzagolDept. IRO, Universit e de Montr 6128, Montreal, Qc, H3C 3J7, lisaTechnical Report 1316, February 2008 AbstractPrevious work has shown that the difficulties in learning deep genera-tive or discriminative models can be overcome by an initial unsupervisedlearning step that maps inputs to useful intermediate representations. Weintroduce and motivate a new training principle for unsupervised learningof a representation based on the idea of making the learned representa-tions Robust to partial corruption of the input pattern. This approach canbe used to train autoencoders, and these Denoising autoencoders can bestacked to initialize deep architectures.
A hallmark of this is our human ability to recognize partially occluded or corrupted images. Further evidence is our ability to form a high level concept associated to multiple modalities (such as image and sound) and recall it even when some of the modalities are missing. To validate our hypothesis and assess its usefulness as one of the guiding
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