Invert to Learn to Invert

Understand

Iterative learning to infer approaches have become popular solvers for inverse problems.

• However, their memory requirements during training grow linearly with model depth, limiting in practice model expressiveness.
• In this work, we propose an iterative inverse model with constant memory that relies on invertible networks to avoid storing intermediate activations.
• As a result, the proposed approach allows us to train models with 400 layers on 3D volumes in an MRI image reconstruction task.