Related papers: Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

URL: http://arxiv.org/abs/2202.05265v1
Date: Thu, 10 Feb 2022 18:59:56 GMT
Title: Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Authors: Anastasios N Angelopoulos, Amit P Kohli, Stephen Bates, Michael I Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, and Yaniv Romano
Abstract summary: We show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value. We evaluate our procedure on three image-to-image regression tasks.
Score: 88.20869695803631
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model's mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems. In particular, we show how to derive uncertainty intervals around each pixel that are guaranteed to contain the true value with a user-specified confidence probability. Our methods work in conjunction with any base machine learning model, such as a neural network, and endow it with formal mathematical guarantees -- regardless of the true unknown data distribution or choice of model. Furthermore, they are simple to implement and computationally inexpensive. We evaluate our procedure on three image-to-image regression tasks: quantitative phase microscopy, accelerated magnetic resonance imaging, and super-resolution transmission electron microscopy of a Drosophila melanogaster brain.

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