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A network supporting deep unsupervised learning is presented. The network isan autoencoder with lateral shortcut connections from the encoder to decoder ateach level of the hierarchy. The lateral shortcut connections allow the higherlevels of the hierarchy to focus on abstract invariant features. While standardautoencoders are analogous to latent variable models with a single layer ofstochastic variables, the proposed network is analogous to hierarchical latentvariables models. Learning combines denoising autoencoder and denoising sourcesseparation frameworks. Each layer of the network contributes to the costfunction a term which measures the distance of the representations produced bythe encoder and the decoder. Since training signals originate from all levelsof the network, all layers can learn efficiently even in deep networks. Thespeedup offered by cost terms from higher levels of the hierarchy and theability to learn invariant features are demonstrated in experiments.

From neural PCA to deep unsupervised learning