Learning to Bound: A Generative Cramér-Rao Bound

Hai Victor Habi, Hagit Messer, Yoram Bresler

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء


The Cramér-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain the CRB, requires an analytical expression for the likelihood of the measurements given the parameters, or equivalently a precise and explicit statistical model for the data. In many applications, such a model is not available. Instead, this work introduces a novel approach to approximate the CRB using data-driven methods, which removes the requirement for an analytical statistical model. This approach is based on the recent success of deep generative models in modeling complex, high-dimensional distributions. Using a learned normalizing flow model, we model the distribution of the measurements and obtain an approximation of the CRB, which we call Generative Cramér-Rao Bound (GCRB). Numerical experiments on simple problems validate this approach, and experiments on two image processing tasks of image denoising and edge detection with a learned camera noise model demonstrate its power and benefits.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)1216-1231
عدد الصفحات16
دوريةIEEE Transactions on Signal Processing
مستوى الصوت71
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2023
منشور خارجيًانعم

ملاحظة ببليوغرافية

Publisher Copyright:
© 1991-2012 IEEE.


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