https://openreview.net/pdf?id=HkSOlP9lg
Inverse problems are typically solved by first defining a model and then choosing an inference procedure. With this separation of modeling from inference, inverse problems can be framed in a modular way. For example, variational inference can be applied to a broad class of models. The modularity, however, typically goes away after model parameters have been trained under a chosen inference procedure. During training, model and inference often interact in a way that the model parameters will ultimately be adapted to the chosen inference procedure, posing the two components inseparable after training. But if model and inference become inseperable after training, why separate them in the first place?
We propose a novel learning framework which abandons the dichotomy between model and inference. Instead, we introduce Recurrent Inference Machines (RIM), a class of recurrent neural networks (RNN), that directly learn to solve inverse problems.
We demonstrate the effectiveness of RIMs in experiments on various image reconstruction tasks. We show empirically that RIMs exhibit the desirable convergence behavior of classical inference procedures, and that they can outperform state-of-the-art methods when trained on specialized inference tasks.
Our approach bridges the gap between inverse problems and deep learning, providing a framework for fast progression in the field of inverse problems.
