Inference in Deep Networks in High Dimensions

Deep generative networks provide a powerful tool for modeling complex data in a wide range of applications. In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from the outputs. Inference is also required for sampling during stochastic training of these generative models. This paper considers inference in a deep stochastic neural network where the parameters (e.g., weights, biases and activation functions) are known and the problem is to estimate the values of the input and hidden units from the output. A novel and computationally tractable inference method called Multi-Layer Vector Approximate Message Passing (ML-VAMP) is presented. Our main contribution shows that the mean-squared error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit. In addition, the MSE achieved by ML-VAMP matches the Bayes optimal value recently postulated by Reeves when certain fixed point equations have unique solutions.