Extracting and Composing Robust Features with Denoising ...

Extracting and Composing Robust Features with DenoisingAutoencodersPascal e de Montr eal, Dept. IRO, CP 6128, Succ. Centre-Ville, Montral, Qubec, H3C 3J7, CanadaAbstractPrevious work has shown that the difficul-ties in learning deep generative or discrim-inative models can be overcome by an ini-tial unsupervised learning step that maps in-puts to useful intermediate introduce and motivate a new trainingprinciple for unsupervised learning of a rep-resentation based on the idea of making thelearned representations Robust to partial cor-ruption of the input pattern. This approachcan be used to train autoencoders, and thesedenoising autoencoders can be stacked to ini-tialize deep architectures.

by optimizing a local unsupervised criterion. Each ... [0,1]d0 through a deterministic mapping y = f ... scent algorithm, in addition to picking an input sam-ple from the training set, we will also produce a ran-dom corrupted version of it, and take a gradient step

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  Feature, With, Optimizing, Through, Robust, Picking, Extracting, Composing, Extracting and composing robust features with denoising, Denoising

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