Workshop Summary

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.

Schedule

The workshop schedule is aligned with 7:30 AM to 4 PM PT; please see this converter for conversion to your specific time zone.

The workshop is livestreamed at the NeurIPS workshop webpage. To access this website you need to register for the conference at https://neurips.cc/ and be logged in.

The videos of the talk can also be previewed as of now through the NeurIPS workshop webpage. The livestream will play the videos, and each of the talks is followed by a live discussion. You can attend the live discussion either through the live stream or you can join the zoom session. Questions should be asked through rocketchat, and a co-organizer will moderate.

During the designated discussion sessions you can talk to the respective speakers in gather.town. To get the gather.town link, visit the official NeurIPS schedule at the NeurIPS workshop webpage.

The poster session is also on gather.town. The gather.town link is accessible through the the NeurIPS workshop webpage.

Time Event
7:30 Newcomer presentation
7:55 Opening Remarks
8:00 Victor Lempitsky (Skoltech): Generative Models for Landscapes and Avatars
8:30 Thomas Pock (TU Graz): Variational Networks
9:00 Contributed talk 1: Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, John M. Pauly: Risk Quantification in Deep MRI Reconstruction
9:15 Contributed talk 2: Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon: GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
9:30 Break and Discussion with Victor Lempinsky, Thomas Pock, and Erich Kobler
10:00 Rebecca Willett (University of Chicago): Model Adaptation for Inverse Problems in Imaging
10:30 Stefano Ermon (Stanford): Generative Modeling via Denoising
11:00 Contributed talk 3: Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price: Compressed Sensing with Approximate Priors via Conditional Resampling
11:15 Chris Metzler: Approximate Message Passing (AMP) Algorithms for Computational Imaging
11:30 Discussion with Rebecca Willett and Stefano Emron
12:00 Break
1:00 Poster session
2:00 Peyman Milanfar (Google) - Denoising as Building Block Theory and Applications
2:30 Rachel Ward (UT Austin)
3:00 Larry Zitnick (Facebook AI Reseach) - fastMRI
3:30 Discussion with Peyman Milanfar, Rachel Ward, and Larry Zitnick
4:00 End of official program

Accepted papers

All accepted papers are available on openreview:

  1. Approximate Probabilistic Inference with Composed Flows
    Jay Whang, Erik Lindgren, Alex Dimakis

  2. Bayesian Inference in Physics-Driven Problems with Adversarial Priors
    Dhruv V Patel, Deep Ray, Harisankar Ramaswamy, Assad Oberai

  3. Compressed Sensing with Approximate Priors via Conditional Resampling
    Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price

  4. Compressed Sensing with Invertible Generative Models and Dependent Noise
    Jay Whang, Qi Lei, Alex Dimakis

  5. Deep Learning for Plasma Tomography in Nuclear Fusion
    Diogo R. Ferreira, Pedro J. Carvalho

  6. Deep Learning Initialized Phase Retrieval
    Raunak Manekar, Zhong Zhuang, Kshitij Tayal, Vipin Kumar, Ju Sun

  7. Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
    Zaccharie Ramzi, Benjamin Remy, Francois Lanusse, Jean-Luc Starck, Philippe Ciuciu

  8. GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
    Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon

  9. Generative Tomography Reconstruction
    Matteo Ronchetti, Davide Bacciu

  10. Generator Surgery for Compressed Sensing
    Jung Yeon Park, Niklas Smedemark-Margulies, Max Daniels, Rose Yu, Jan-Willem van de Meent, Paul Hand

  11. Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
    Joseph Dean, Giannis Daras, Alex Dimakis

  12. Learning Spectral Regularizations for Linear Inverse Problems
    Hartmut Bauermeister, Martin Burger, Michael Moeller

  13. Learning to Sample MRI via Variational Information Maximization
    Cagan Alkan, Morteza Mardani, Shreyas Vasanawala, John M. Pauly

  14. Likelihood-Free Inference with Deep Gaussian Processes
    Alexander Aushev, Henri Pesonen, Markus Heinonen, Jukka Corander, Samuel Kaski

  15. Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
    Riccardo Barbano, Zeljko Kereta, Chen Zhang, Andreas Hauptmann, Simon Arridge, Bangti Jin

  16. Risk Quantification in Deep MRI Reconstruction
    Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, John M. Pauly

  17. Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
    Zahra Kadkhodaie, Eero Peter Simoncelli

  18. Towards Neurally Augmented ALISTA
    Freya Behrens, Jonathan Sauder, Peter Jung

  19. Uncertainty-Driven Adaptive Sampling via GANs
    Thomas Sanchez, Igor Krawczuk, Zhaodong Sun, Volkan Cevher

  20. Unlocking Inverse Problems Using Deep Learning: Breaking Symmetries in Phase Retrieval
    Kshitij Tayal, Chieh-Hsin Lai, Raunak Manekar, Zhong Zhuang, Vipin Kumar, Ju Sun

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 workshop edition of the NeurIPS style file, but they may use any other style as long as it has standard font size (11pt) and margins (1in). The paper can have an appendix of unlimited lenght.

Submission at OpenReview will be open from Sep. 1 until the submission deadline on October 9, 2020.

We welcome all submission in the intersection of inverse problems and deep learning including contributions related to robustness and biases, neural network architectures, regularization, optimization methods, datasets, theoretical foundations (including rigorous recovery guarantees, provable convergence, and bounds on representation errors), untrained methods, generative models, end-to-end methods, and applications in imaging, time series, and beyond. We especially encourage submissions related to the following questions:

Important Dates

Organizers

Please email deepinverse@gmail.com with any questions.