![]() ![]() This allows the meta-model to leverage a few ground truth samples for each task while being able to generalize to new imaging tasks. In its bilevel formulation, the outer level uses a supervised loss, that evaluates how well the fine-tuned model performs, while the inner loss can be either supervised or unsupervised, relying only on the measurement operator. We show that the proposed method extends to the unsupervised setting, where no ground truth data is available. Our method trains a meta-model on a diverse set of imaging tasks that allows the model to be efficiently fine-tuned for specific tasks with few fine-tuning steps. ![]() To overcome these limitations, we introduce a novel approach based on meta-learning. Moreover, for each new imaging task, a new model needs to be trained from scratch, wasting time and resources. However, real-world imaging challenges often lack ground truth data, rendering traditional supervised approaches ineffective. They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover. Download a PDF of the paper titled Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers, by Matthieu Terris and 1 other authors Download PDF Abstract:Deep neural networks have become a foundational tool for addressing imaging inverse problems. ![]()
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