  Recent work (Bengio et al., 2013) has shown howDenoising Auto-Encoders(DAE)
become gener-ative models as a density estimator. However,in practice, the
framework suffers from a mixingproblem in the MCMC sampling process and
nodirect method to estimate the test log-likelihood.We consider a directed
model with an stochas-tic identity mapping (simple corruption pro-cess) as an
inference model and a DAE as agenerative model. By cascading these mod-els, we
propose Cascading Denoising Auto-Encoders(CDAE) which can generate samples
ofdata distribution from tractable prior distributionunder the assumption that
probabilistic distribu-tion of corrupted data approaches tractable
priordistribution as the level of corruption increases.This work tries to
answer two questions. On theone hand, can deep directed models be success-fully
trained without intractable posterior infer-ence and difficult optimization of
very deep neu-ral networks in inference and generative mod-els? These are
unavoidable when recent suc-cessful directed model like VAE (Kingma &Welling,
2014) is trained on complex dataset likereal images. On the other hand, can
DAEs getclean samples of data distribution from heavilycorrupted samples which
can be considered oftractable prior distribution far from data mani-fold?
so-called global denoising scheme.Our results show positive responses of
thesequestions and this work can provide fairly simpleframework for generative
models of very com-plex dataset.

Machine Learning