From: NeurIPS.cc/2021/Workshop/Deep_Inverse/Program_Chairs
title: Paper Decision
decision: Accept (Oral)
comment: This paper introduces a new learning algorithm for unrolled networks applied to inverse problems.  Instead of running backprop on the loss between the output of a full unrolled network and a target, it puts loss functions on each fold of the unrolling.  This allows for considerably less memory requirements when doing only a backaprop of a module at a time.  The method is 6x less memory-consuming than the full network backdrop at comparable recovery performance.