Training Adaptive Reconstruction Networks for Inverse Problems

Neural networks are full of promises for the resolution of ill-posed inverse problems. In particular, physics informed learning approaches already seem to progressively gradually replace carefully hand-crafted reconstruction algorithms, for their superior quality. The aim of this paper is twofold. First we show a significant weakness of these networks: they do not adapt efficiently to variations of the forward model. Second, we show that training the network with a family of forward operators allows to solve the adaptivity problem without compromising the reconstruction quality significantly. All our experiments are carefully devised on partial Fourier sampling problems arising in magnetic resonance imaging (MRI).