Image synthesis and efficient data representations for visual machine learning

Driven by the high accuracy and performance of deep learning algorithms in computer vision tasks, we are investigating the use and production of highly realistic training data-sets for computer vision applications.

Moreover, we explore variations of abstracted data representations in order to further understand how data features at different abstraction levels affect the learning process in deep neural networks, and how this can be used to optimize the data generation and usage.

Link to publication

Highly realistic image synthesis for visual machine learning in automotive applications (Picture 1), along with abstracted data representations of the same scene (Picture 2 and 3).

Illustration 7D Labs Illustration: 7D Labs. Bildbehandling och effektiv representation av data för visuell maskininlärning

Illustration: 7D Labs. Bildbehandling och effektiv representation av data för visuell maskininlärning

Researchers

Jonas Unger

Professor

Gabriel Eilertsen

Assistant Professor, Docent

External partner

The publication and work was done in collaboration with Magnus Wrenninge, 7D Labs.

https://7dlabs.com/

WASP research at MIT

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WASP at MIT

Our research environment is located at the division of Media and Information Technology on Campus Norrköping. Our work draws on 20 years of research, focusing on visualization and interaction in the context of AI, autonomous systems and software.