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Inspired by the combination of feedforward and iterative computations in thevirtual cortex, and taking advantage of the ability of denoising autoencodersto estimate the score of a joint distribution, we propose a novel approach toiterative inference for capturing and exploiting the complex joint distributionof output variables conditioned on some input variables. This approach isapplied to image pixel-wise segmentation, with the estimated conditional scoreused to perform gradient ascent towards a mode of the estimated conditionaldistribution. This extends previous work on score estimation by denoisingautoencoders to the case of a conditional distribution, with a novel use of acorrupted feedforward predictor replacing Gaussian corruption. An advantage ofthis approach over more classical ways to perform iterative inference forstructured outputs, like conditional random fields (CRFs), is that it is notany more necessary to define an explicit energy function linking the outputvariables. To keep computations tractable, such energy functionparametrizations are typically fairly constrained, involving only a fewneighbors of each of the output variables in each clique. We experimentallyfind that the proposed iterative inference from conditional score estimation byconditional denoising autoencoders performs better than comparable models basedon CRFs or those not using any explicit modeling of the conditional jointdistribution of outputs.

Image Segmentation by Iterative Inference from Conditional Score Estimation