Explore the words cloud of the SPADE project. It provides you a very rough idea of what is the project "SPADE" about.
The following table provides information about the project.
Coordinator |
TEL AVIV UNIVERSITY
Organization address contact info |
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.
# | ||||
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1 | TEL AVIV UNIVERSITY | IL (TEL AVIV) | coordinator | 1˙499˙375.00 |
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 |
---|---|---|---|
2019 |
Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or ALIGNet 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: |
2019-08-05 | |
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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The information about "SPADE" are provided by the European Opendata Portal: CORDIS opendata.
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