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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.

inputs. Section 2 describes the algorithm in details. Section 3 discusses links with other approaches in the literature. Section 4 is devoted to a closer inspec-tion of the model from different theoretical standpoints. In section 5 we verify empirically if the algorithm leads to a difference in performance. Section 6 concludes the study. 2

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