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

Researchers
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External partner

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

WASP research at MIT
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