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Workshop: Workshop on Deep Learning and Inverse Problems

Reinhard Heckel, Paul Hand, Richard Baraniuk, Lenka Zdeborová, Soheil Feizi

2020-12-11T07:30:00-08:00 - 2020-12-11T16:00:00-08:00
Abstract: Learning-based methods, and in particular deep neural networks, have emerged as highly successful and universal tools for image and signal recovery and restoration. They achieve state-of-the-art results on tasks ranging from image denoising, image compression, and image reconstruction from few and noisy measurements. They are starting to be used in important imaging technologies, for example in GEs newest computational tomography scanners and in the newest generation of the iPhone.

The field has a range of theoretical and practical questions that remain unanswered. In particular, learning and neural network-based approaches often lack the guarantees of traditional physics-based methods. Further, while superior on average, learning-based methods can make drastic reconstruction errors, such as hallucinating a tumor in an MRI reconstruction or turning a pixelated picture of Obama into a white male.

This virtual workshop aims at bringing together theoreticians and practitioners in order to chart out recent advances and discuss new directions in deep neural network-based approaches for solving inverse problems in the imaging sciences and beyond. NeurIPS, with its visibility and attendance by experts in machine learning, offers the ideal frame for this exchange of ideas. We will use this virtual format to make this topic accessible to a broader audience than the in-person meeting is able to as described below.


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2020-12-11T07:30:00-08:00 - 2020-12-11T07:55:00-08:00
Newcomer presentation
Reinhard Heckel, Paul Hand
2020-12-11T07:55:00-08:00 - 2020-12-11T08:00:00-08:00
Opening Remarks
Reinhard Heckel, Paul Hand, Soheil Feizi, Lenka Zdeborová, Richard Baraniuk
2020-12-11T08:00:00-08:00 - 2020-12-11T08:30:00-08:00
Victor Lempitsky - Generative Models for Landscapes and Avatars
Victor Lempitsky
2020-12-11T08:30:00-08:00 - 2020-12-11T09:00:00-08:00
Thomas Pock - Variational Networks
Thomas Pock
2020-12-11T09:00:00-08:00 - 2020-12-11T09:15:00-08:00
Risk Quantification in Deep MRI Reconstruction
Vineet Edupuganti
2020-12-11T09:15:00-08:00 - 2020-12-11T09:30:00-08:00
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
Sungmin Cha
2020-12-11T09:30:00-08:00 - 2020-12-11T10:00:00-08:00
2020-12-11T10:00:00-08:00 - 2020-12-11T10:30:00-08:00
Rebecca Willett - Model Adaptation for Inverse Problems in Imaging
Rebecca Willett
2020-12-11T10:30:00-08:00 - 2020-12-11T11:00:00-08:00
Stefano Emron - Generative Modeling via Denoising
Stefano Ermon
2020-12-11T11:00:00-08:00 - 2020-12-11T11:15:00-08:00
Compressed Sensing with Approximate Priors via Conditional Resampling
Ajil Jalal
2020-12-11T11:15:00-08:00 - 2020-12-11T11:30:00-08:00
Chris Metzler - Approximate Message Passing (AMP) Algorithms for Computational Imaging
Christopher A Metzler
2020-12-11T11:30:00-08:00 - 2020-12-11T12:00:00-08:00
2020-12-11T13:00:00-08:00 - 2020-12-11T14:00:00-08:00
Poster Session
2020-12-11T14:00:00-08:00 - 2020-12-11T14:30:00-08:00
Peyman Milanfar - Denoising as Building Block Theory and Applications
Peyman Milanfar
2020-12-11T14:30:00-08:00 - 2020-12-11T15:00:00-08:00
Rachel Ward
Rachel Ward
2020-12-11T15:00:00-08:00 - 2020-12-11T15:30:00-08:00
Larry Zitnick - fastMRI
Larry Zitnick
2020-12-11T15:30:00-08:00 - 2020-12-11T16:00:00-08:00