Deep learning based techniques achieve state-of-the-art results in a wide range of image reconstruction tasks like compressed sensing. These methods almost always have hyperparameters, such as the weight coefficients that balance the different terms in the optimized loss function. The typical approach is to train the model for a hyperparameter setting determined with some empirical or theoretical justification. Thus, at inference time, the model can only compute reconstructions corresponding to the pre-determined hyperparameter values. In this work, we present a hypernetwork based approach, called HyperRecon, to train reconstruction models that are agnostic to hyperparameter settings. At inference time, HyperRecon can efficiently produce diverse reconstructions, which would each correspond to different hyperparameter values. In this framework, the user is empowered to select the most useful output(s) based on their own judgement. We demonstrate our method in compressed sensing, super-resolution and denoising tasks, using two large-scale and publicly-available MRI datasets. Our code is available at https://github.com/alanqrwang/hyperrecon.

翻译：深层学习技术在压缩感测等广泛的图像重建任务中取得了最先进的成果。这些方法几乎总是有超参数,例如,在优化损失功能中平衡不同条件的重量系数。典型的方法是用一些经验或理论理由来训练超参数设置模型。因此,在推论时间,该模型只能计算与预先确定的超参数值相对应的重建情况。在这项工作中,我们提出了一个超网络方法,称为HyperRecon,用于培训对超参数设置具有敏感性的重建模型。在推论时间,HyperRecon可以有效地产生不同的重建情况,每个情况都与不同的超参数值相对应。在这个框架内,用户有权根据自己的判断选择最有用的产出。我们用两个大尺度和公开可用的MRISet来展示我们的压缩感测、超分辨率和去音量任务的方法。我们的代码可以在https://github.com/alanrwang/hyperrecon上查到。