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DeepInternal SIGNED

Going Deep and Blind with Internal Statistics

Total Cost €


EC-Contrib. €






Project "DeepInternal" data sheet

The following table provides information about the project.


Organization address
address: HERZL STREET 234
postcode: 7610001

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 2˙466˙940 €
 EC max contribution 2˙466˙940 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-ADG
 Funding Scheme ERC-ADG
 Starting year 2018
 Duration (year-month-day) from 2018-05-01   to  2023-04-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    WEIZMANN INSTITUTE OF SCIENCE IL (REHOVOT) coordinator 2˙466˙940.00


 Project objective

Unsupervised visual inference can often be performed by exploiting the internal redundancy inside a single visual datum (an image or a video). The strong repetition of patches inside a single image/video provides a powerful data-specific prior for solving a variety of vision tasks in a “blind” manner: (i) Blind in the sense that sophisticated unsupervised inferences can be made with no prior examples or training; (ii) Blind in the sense that complex ill-posed Inverse-Problems can be solved, even when the forward degradation is unknown.

While the above fully unsupervised approach achieved impressive results, it relies on internal data alone, hence cannot enjoy the “wisdom of the crowd” which Deep-Learning (DL) so wisely extracts from external collections of images, yielding state-of-the-art (SOTA) results. Nevertheless, DL requires huge amounts of training data, which restricts its applicability. Moreover, some internal image-specific information, which is clearly visible, remains unexploited by today's DL methods. One such example is shown in Fig.1.

We propose to combine the power of these two complementary approaches – unsupervised Internal Data Recurrence, with Deep Learning, to obtain the best of both worlds. If successful, this will have several important outcomes including: • A wide range of low-level & high-level inferences (image & video). • A continuum between Internal & External training – a platform to explore theoretical and practical tradeoffs between amount of available training data and optimal Internal-vs-External training. • Enable totally unsupervised DL when no training data are available. • Enable supervised DL with modest amounts of training data. • New applications, disciplines and domains, which are enabled by the unified approach. • A platform for substantial progress in video analysis (which has been lagging behind so far due to the strong reliance on exhaustive supervised training data).


year authors and title journal last update
List of publications.
2019 Yosef Gandelsman, Assaf Shocher, Michal Irani
\"\"\"Double-DIP\"\": Unsupervised Image Decomposition via Coupled Deep-Image-Priors\"
published pages: , ISSN: , DOI:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020-01-29
2019 Assaf Shocher, Shai Bagon, Phillip Isola, Michal Irani
\"InGAN: Capturing and Remapping the \"\"DNA\"\" of a Natural Image\"
published pages: , ISSN: , DOI:
International Conference on Computer Vision (ICCV) 2020-01-29
2019 Sefi Bell-Kligler, Assaf Shocher, Michal Irani
“KernelGAN”: Blind Super-Resolution Kernel Estimation using an Internal-GAN
published pages: , ISSN: , DOI:
Conference on Neural Information Processing Systems (NeurIPS) 2020-01-29
2019 Shany Grossman, Guy Gaziv, Erin M. Yeagle, Michal Harel, Pierre Mégevand, David M. Groppe, Simon Khuvis, Jose L. Herrero, Michal Irani, Ashesh D. Mehta, Rafael Malach
Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-019-12623-6
Nature Communications 10/1 2020-01-29
2019 Roman Beliy, Guy Gaziv, Assaf Hoogi, Fracesca Strappini, Tal Golan, Michal Irani
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
published pages: , ISSN: , DOI:
Conference on Neural Information Processing Systems (NeurIPS) 2020-01-29

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The information about "DEEPINTERNAL" are provided by the European Opendata Portal: CORDIS opendata.

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