Geometric Deep Learning
Research Project, 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.


Fredrik Kahl (contact)

Imaging and Image Analysis

Axel Flinth

Imaging and Image Analysis


Chalmers AI Research Centre

Gothenburg, Sweden


Chalmers AI Research Centre (CHAIR)

Funding Chalmers participation during 2020–2022

Related Areas of Advance and Infrastructure

Information and Communication Technology

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