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Iterative learning to infer approaches have become popular solvers forinverse problems. However, their memory requirements during training growlinearly with model depth, limiting in practice model expressiveness. In thiswork, we propose an iterative inverse model with constant memory that relies oninvertible networks to avoid storing intermediate activations. As a result, theproposed approach allows us to train models with 400 layers on 3D volumes in anMRI image reconstruction task. In experiments on a public data set, wedemonstrate that these deeper, and thus more expressive, networks performstate-of-the-art image reconstruction.

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