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. The algorithm canbe motivated from a manifold learning andinformation theoretic perspective or from agenerative model perspective.

Extracting and Composing Robust Features with Denoising Autoencoders explicit criteria a good intermediate representation should satisfy. Obviously, it should at a minimum re-tain a certain amount of “information” about its input, while at the same time being constrained to a given form (e.g. a real-valued vector of a given size in the

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