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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.
Extracting and Composing Robust Features with Denoising Autoencoders 2.3. The Denoising Autoencoder To test our hypothesis and enforce robustness to par-tially destroyed inputs we modify the basic autoen-coder we just described. We will now train it to recon-struct a clean “repaired” input from a corrupted, par-tially destroyed one.
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