ation based on the idea of
making the learned representations robust to partial corruption of the input
pattern. This approach can be used to train autoencoders, and these denoising
autoencoders can be stacked to initialize deep architectures. The algorithm
can be motivated from a manifold learning and information theoretic
perspective or from a generative model perspective. Comparative experiments
clearly show the surprising advantage of corrupting the input of autoencoders
on a pattern classification benc