Скачать или смотреть MDL | Prof. Jong Chul Ye | Generative and self-supervised learning approaches for inverse problems

MDL | Prof. Jong Chul Ye | Generative and self-supervised learning approaches for inverse problems without paired training data

Speaker: Professor Jong Chul Ye (Korea Advanced Institute of Science and Technology (KAIST))
Date: 12th Oct 2021 - 9:00 to 10:00
Venue: INI Seminar Room 1
Title: Generative and self-supervised learning approaches for inverse problems without paired training data
Event: (MDL) Mathematics of deep learning
Abstract: Chair: Martin Burger
Recently, deep learning approaches have become the main research frontier for image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various inverse problem applications. In this talk, we overview these approaches from a coherent perspective in the context of classical inverse problems and discuss their various applications. In particular, the cycleGAN approach and a recent Noise2Score approach for unsupervised learning will be explained in detail using optimal transport theory and Tweedie’s formula with score matching.

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