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Robust, Explainable Deep Networks in Computer Vision

Project description

Helping computers see things better

The creation of convolutional neural networks (CNNs – a class of deep learning algorithms) has revolutionised computer vision by enabling computers to 'see' things and react to them. However, CNNs have not solved all issues. For instance, large amounts of labelled data are still required for training, and this is not possible in all potential application areas. Moreover, the majority of deep networks in computer vision are weak in terms of explainability. The EU-funded RED project will work to advance the robustness and explainability of deep networks in computer vision. It will explore structured network designs, probabilistic methods and hybrid generative/discriminative models. It will also advance the research on how to assess robustness and aspects of explainability through dedicated datasets and metrics, considering the challenges of 3D scene analytics.

Call for proposal

ERC-2019-COG
See other projects for this call

Funding Scheme

ERC-COG - Consolidator Grant

Host institution

TECHNISCHE UNIVERSITAT DARMSTADT
Address
Karolinenplatz 5
64289 Darmstadt
Germany
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 999 814

Beneficiaries (1)

TECHNISCHE UNIVERSITAT DARMSTADT
Germany
EU contribution
€ 1 999 814
Address
Karolinenplatz 5
64289 Darmstadt
Activity type
Higher or Secondary Education Establishments