  Inspired by the combination of feedforward and iterative computations in the
virtual cortex, and taking advantage of the ability of denoising autoencoders
to estimate the score of a joint distribution, we propose a novel approach to
iterative inference for capturing and exploiting the complex joint distribution
of output variables conditioned on some input variables. This approach is
applied to image pixel-wise segmentation, with the estimated conditional score
used to perform gradient ascent towards a mode of the estimated conditional
distribution. This extends previous work on score estimation by denoising
autoencoders to the case of a conditional distribution, with a novel use of a
corrupted feedforward predictor replacing Gaussian corruption. An advantage of
this approach over more classical ways to perform iterative inference for
structured outputs, like conditional random fields (CRFs), is that it is not
any more necessary to define an explicit energy function linking the output
variables. To keep computations tractable, such energy function
parametrizations are typically fairly constrained, involving only a few
neighbors of each of the output variables in each clique. We experimentally
find that the proposed iterative inference from conditional score estimation by
conditional denoising autoencoders performs better than comparable models based
on CRFs or those not using any explicit modeling of the conditional joint
distribution of outputs.

Computer Vision and Pattern Recognition