Skip to the content.

Workshop Description

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. They are also used in inverse problems beyond imaging, including for solving inverse problems arrising in communications, signal processing, and even on non-Euclidean data suchs as graphs.

The field has a range of theoretical and practical questions that remain unanswered, including questions about guarantees, robustness, architectural design, the role of learning, domain specific applications, and more. This virtual workshop aims at bringing together theoreticians and practitioners in order to chart out recent advances and discuss new directions in deep learning-based approaches for solving inverse problems in the imaging sciences and beyond.

Call for Papers and Submission Instructions

We invite researchers to submit anonymous papers of up to 4 pages (excluding references and appendices) which will be considered for contributed workshop papers. No specific formatting is required. Authors are encouraged to use the NeurIPS style file, but they may use any other style as long as it has standard font size (11pt) and margins (1in).

Submission at OpenReview is open now until the submission deadline on September 26, 2021.

We welcome all submissions in the intersection of inverse problems and deep learning, including but not limited to submissions on the following topics:

Important Dates

Speakers

Jonas Adler Muyinatu Bell Alex Dimakis
Jonas Adler
DeepMind
Muyinatu Bell
John Hopkins University
Alex Dimakis
UT Austin
Kerstin Hammernik Stan Osher Carola Schönlieb
Kerstin Hammernik
Technical University of Munich
Stan Osher
UCLA
Carola Schönlieb
Cambridge University
Gordon Wetzstein    
Gordon Wetzstein
Stanford University
   

Organizers

Paul Hand Reinhard Heckel Christopher Metzler
Paul Hand
Northeastern University
Reinhard Heckel
Technical University of Munich
Christopher Metzler
University of Maryland
Mahdi Soltanolkotabi Rebecca Willett  
Mahdi Soltanolkotabi
University of Southern California
Rebecca Willett
University of Chicago
 

Please email deepinverse@gmail.com with any questions.