Innovating Works

DeepInternal

Financiado
Going Deep and Blind with Internal Statistics
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-s... 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). ver más
31/10/2023
WEIZMANN
2M€
Duración del proyecto: 66 meses Fecha Inicio: 2018-04-24
Fecha Fin: 2023-10-31

Línea de financiación: concedida

El organismo H2020 notifico la concesión del proyecto el día 2023-10-31
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
ERC-2017-ADG: ERC Advanced Grant
Cerrada hace 7 años
Presupuesto El presupuesto total del proyecto asciende a 2M€
Líder del proyecto
WEIZMANN INSTITUTE OF SCIENCE No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5