Opendata, web and dolomites


from SPArsity to DEep learning

Total Cost €


EC-Contrib. €






Project "SPADE" data sheet

The following table provides information about the project.


Organization address
address: RAMAT AVIV
city: TEL AVIV
postcode: 69978

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Israel [IL]
 Total cost 1˙499˙375 €
 EC max contribution 1˙499˙375 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2017
 Duration (year-month-day) from 2017-10-01   to  2022-09-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TEL AVIV UNIVERSITY IL (TEL AVIV) coordinator 1˙499˙375.00


 Project objective

Lately, deep learning (DL) has become one of the most powerful machine learning tools with ground-breaking results in computer vision, signal & image processing, language processing, and many other domains. However, one of its main deficiencies is the lack of theoretical foundation. While some theory has been developed, it is widely agreed that DL is not well-understood yet.

A proper understanding of the learning mechanism and architecture is very likely to broaden the great success to new fields and applications. In particular, it has the promise of improving DL performance in the unsupervised regime and on regression tasks, where it is currently lagging behind its otherwise spectacular success demonstrated in massively-supervised classification problems.

A somewhat related and popular data model is based on sparse-representations. It led to cutting-edge methods in various fields such as medical imaging, computer vision and signal & image processing. Its success can be largely attributed to its well-established theoretical foundation, which boosted the development of its various ramifications. Recent work suggests a close relationship between this model and DL, although this bridge is not fully clear nor developed.

This project revolves around the use of sparsity with DL. It aims at bridging the fundamental gap in the theory of DL using tools applied in sparsity, highlighting the role of structure in data as the foundation for elucidating the success of DL. It also aims at using efficient DL methods to improve the solution of problems using sparse models. Moreover, this project pursues a unified theoretical framework merging sparsity with DL, in particular migrating powerful unsupervised learning concepts from the realm of sparsity to that of DL. A successful marriage between the two fields has a great potential impact of giving rise to a new generation of learning methods and architectures and bringing DL to unprecedented new summits in novel domains and tasks.


year authors and title journal last update
List of publications.
2019 Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
published pages: 1-14, ISSN: 0730-0301, DOI: 10.1145/3267347
ACM Transactions on Graphics 38/1 2019-08-05
2018 Shay Zucker, Raja Giryes
Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets
published pages: 147, ISSN: 1538-3881, DOI: 10.3847/1538-3881/aaae05
The Astronomical Journal 155/4 2019-09-04
2019 Tom Tirer, Raja Giryes
Image Restoration by Iterative Denoising and Backward Projections
published pages: 1220-1234, ISSN: 1057-7149, DOI: 10.1109/tip.2018.2875569
IEEE Transactions on Image Processing 28/3 2019-08-05
2018 Harel Haim, Shay Elmalem, Raja Giryes, Alex M. Bronstein, Emanuel Marom
Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask
published pages: 298-310, ISSN: 2333-9403, DOI: 10.1109/tci.2018.2849326
IEEE Transactions on Computational Imaging 4/3 2019-08-05
2019 Elad Plaut, Raja Giryes
A Greedy Approach to $ell_{0,infty}$-Based Convolutional Sparse Coding
published pages: 186-210, ISSN: 1936-4954, DOI: 10.1137/18m1165116
SIAM Journal on Imaging Sciences 12/1 2019-08-05
2018 Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
Class-Aware Fully Convolutional Gaussian and Poisson Denoising
published pages: 5707-5722, ISSN: 1057-7149, DOI: 10.1109/tip.2018.2859044
IEEE Transactions on Image Processing 27/11 2019-08-05
2018 Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogerio Feris, Raja Giryes, Alex Bronstein
Delta-encoder: an effective sample synthesis method for few-shot object recognition
published pages: , ISSN: , DOI:
2018 Raja Giryes, Yonina C. Eldar, Alex M. Bronstein, Guillermo Sapiro
Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems
published pages: 1676-1690, ISSN: 1053-587X, DOI: 10.1109/tsp.2018.2791945
IEEE Transactions on Signal Processing 66/7 2019-08-05
2019 Eli Schwartz, Raja Giryes, Alex M. Bronstein
DeepISP: Toward Learning an End-to-End Image Processing Pipeline
published pages: 912-923, ISSN: 1057-7149, DOI: 10.1109/tip.2018.2872858
IEEE Transactions on Image Processing 28/2 2019-08-05
2018 Shay Elmalem, Raja Giryes, Emanuel Marom
Learned phase coded aperture for the benefit of depth of field extension
published pages: 15316, ISSN: 1094-4087, DOI: 10.1364/oe.26.015316
Optics Express 26/12 2019-08-05

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