One Network to Solve Them All — Solving Linear Inverse Problems using Deep Projection Models

We now describe the architecture of the networks used in the paper. We use exponential linear unit (elu) [1] as activation function. We also use virtual batch normalization [6], where the reference batch size bref is equal to the batch size used for stochastic gradient descent. We weight the reference batch with bref bref+1 . We define some shorthands for the basic components used in the networks.