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.

How to attend the workshop

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 livestream will play the talks, 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 either through rocketchat or in the chat function in the zoom session, and a co-organizer will moderate.

During the designated discussion sessions you can talk to the respective speakers in gather.town.

The poster session is also on gather.town. The gather.town link is accessible only when logged in at neurips.cc and registered for the conference.

Schedule

The workshop schedule is aligned with 7:55 AM to 4 PM PT; please see this converter for conversion to your specific time zone.

Time (PST)	Event
7:55	Opening Remarks
8:00	Alex Dimakis (UT Austin) Talk
8:30	Muyinatu Bell (John Hopkins University) Talk
9:00	Contributed talk 1: Batu Ozturkler, Arda Sahiner, Tolga Ergen, Arjun D Desai, John M. Pauly, Shreyas Vasanawala, Morteza Mardani, Mert Pilanci: Greedy Learning for Large-Scale Neural MRI Reconstruction
9:15	Contributed talk 2: Alireza Naderi, Yaniv Plan: Beyond Independent Measurements: General Compressed Sensing with GNN Application
9:30	Break and Discussion with Alex Dimakis and Muyinatu Bell at gather.town
10:00	Contributed talk 3: Eric Markley, Fanglin Linda Liu, Michael Kellman, Nick Antipa, Laura Waller: Physics-Based Learned Diffuser for Single-shot 3D Imaging
10:15	Kerstin Hammernik (TUM) Talk
10:45	Carola Schönlieb (University of Cambridge) Talk
11:15	Break and discussion with Kerstin Hammernik and Carola Schönlieb at gather.town
11:45	Break
12:45	Poster session at gather.town
2:00	Gordon Wetzstein (Stanford) Talk
2:30	Stan Osher (UCLA) Talk
3:00	Jonas Adler (DeepMind) Talk
3:30	Discussion with Gordon Wetzstein, Stan Osher, and Jonas Adler at gather.town
4:00	End of official program

Speakers

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

Accepted papers

All accepted papers are available on openreview:

1. A Closer Look at Reference Learning for Fourier Phase Retrieval
   Tobias Uelwer, Nick Rucks, Stefan Harmeling

2. Bayesian Inference in Physics-Based Nonlinear Flame Models
   Maximilian L. Croci, Ushnish Sengupta, Matthew P Juniper

3. Beyond Independent Measurements: General Compressed Sensing with GNN Application
   Alireza Naderi, Yaniv Plan

4. DARTS for Inverse Problems: a Study on Stability
   Jonas Geiping, Jovita Lukasik, Margret Keuper, Michael Moeller

5. Deep subspace learning for efficient reconstruction of spatiotemporal imaging data
   Christopher Michael Sandino, Frank Ong, Siddharth Srinivasan Iyer, Adam Bush, Shreyas Vasanawala

6. Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs
   Deep Ray, Dhruv V Patel, Harisankar Ramaswamy, Assad Oberai

7. Functional Response Conditional Variational Auto-Encoders for Inverse Design of Metamaterials
   Che Wang, Yuhao Fu, Ke Deng, Chunlin Ji

8. Greedy Learning for Large-Scale Neural MRI Reconstruction
   Batu Ozturkler, Arda Sahiner, Tolga Ergen, Arjun D Desai, John M. Pauly, Shreyas Vasanawala, Morteza Mardani, Mert Pilanci

9. Invertible Learned Primal-Dual
   Jevgenija Rudzusika, Buda Bajic, Ozan Öktem, Carola-Bibiane Schönlieb, Christian Etmann

10. Learning convex regularizers satisfying the variational source condition for inverse problems
    Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger

11. Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods
    Pakshal Bohra, Dimitris Perdios, Alexis Goujon, Sébastien Emery, Michael Unser

12. Learning Structured Sparse Matrices for Signal Recovery via Unrolled Optimization
    Jonathan Sauder, Martin Genzel, Peter Jung

13. Likelihood-Free Inference in State-Space Models with Unknown Dynamics
    Alexander Aushev, Thong Anh Tran, Henri Pesonen, Andrew Howes, Samuel Kaski

14. Matching Plug-and-Play Algorithms to the Denoiser
    Saurav K Shastri, Rizwan Ahmad, Christopher Metzler, Philip Schniter

15. Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple Contrasts
    Victoria Liu, Kanghyun Ryu, Cagan Alkan, John M. Pauly, Shreyas Vasanawala

16. Near-Exact Recovery for Sparse-View CT via Data-Driven Methods
    Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

17. NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
    Liyue Shen, John M. Pauly, Lei Xing

18. PANOM: Automatic Hyper-parameter Tuning for Inverse Problems
    Tianci Liu, Quan Zhang, Qi Lei

19. Photoacoustic imaging with conditional priors from normalizing flows
    Rafael Orozco, Ali Siahkoohi, Gabrio Rizzuti, Tristan van Leeuwen, Felix Johan Herrmann

20. Photon-Limited Deblurring using Algorithm Unrolling
    Yash Sanghvi, Abhiram Gnanasambandam, Stanley Chan

21. Physics-Based Learned Diffuser for Single-shot 3D Imaging
    Eric Markley, Fanglin Linda Liu, Michael Kellman, Nick Antipa, Laura Waller

22. PLUGIn-CS: A simple algorithm for compressive sensing with generative prior
    Babhru Joshi, Xiaowei Li, Yaniv Plan, Ozgur Yilmaz

23. ReFIn: A Refinement Approach for Video Frame Interpolation
    Saikat Dutta, Anurag Mittal

24. Robust Compressed Sensing MR Imaging with Deep Generative Priors
    Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alex Dimakis, Jonathan Tamir

25. Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
    Yang Song, Liyue Shen, Lei Xing, Stefano Ermon

26. Sparse deep computer-generated holography for optical microscopy
    Alex Liu, Yi Xue, Laura Waller

27. SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
    Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M. Pauly, Akshay Chaudhari

28. Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction
    Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akcakaya

Organizers

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

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:

• Deep learning based approaches can make drastic reconstruction errors and may introduce biases. How common are such reconstruction problems, can they be alleviated, and if yes, how?

• Deep learning based approaches often lack the guarantees of the traditional physics based methods. What theoretical results are necessary and possible?

• Coordinate-based signal representations (e.g., NeRF and SIREN) and untrained convolutional neural networks have shown that neural networks alone, without any learning, can give excellent reconstruction performance. Is it possible to achieve state-of-the art performance without or little training data?

Important Dates

• Submission Deadline: September 17, 2021 extended until September 26, 2021, AoE (this extension is final).
• Notification of acceptance: October 15, 2021.
• SlidesLive upload for speaker videos: November 1, 2021
• Workshop: Monday December 13, 2021.

Please email deepinverse@gmail.com with any questions.