Geometrisk djupinlärning
Forskningsprojekt , 2020 – 2022

The project aims to investigate how inherent geometrical properties of the data can be incorporated in deep learning methods. To be concrete, consider a supervised learning task. The data set having an ‘inherent geometrical property’ can mathematically expressed as the labels depending either invariantly or equivariantly on the data point with respect to some group action. Can this invariance/equivariance be guaranteed by a certain architecture design? Convolutional neural networks are in fact an example of such an architecture – if the input image is translated, the output of a convolutional layer is translated with it.

Deltagare

Fredrik Kahl (kontakt)

Professor vid Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Axel Flinth

Doktor vid Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Samarbetspartners

Chalmers AI Research Centre

Gothenburg, Sweden

Finansiering

Chalmers AI-forskningscentrum (CHAIR)

Finansierar Chalmers deltagande under 2020–2022

Relaterade styrkeområden och infrastruktur

Informations- och kommunikationsteknik

Styrkeområden

Mer information

Senast uppdaterat

2020-12-14