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).