The Information plane can be used to gain insight and theoretical understanding of neural networks.


Opening the black box of AI

Although Deep Neural Networks (DNNs) are at the core of most state–of–the art systems in computer vision, the theoretical understanding and explainability possibilities of such networks is still not at a satisfactory level.

To a large degree, our user partner’s applications involve imaging the unseen – the inside of the human body, the sea, and the surface of the earth seen from space independent of daylight and weather conditions. Impact of innovative technology for users depends on trust. A limitation of deep learning models is that there is no generally accepted solution for how to open the “black-box” of the deep network to provide explainable decisions which can be relied on to be trustworthy.

Visual Intelligence aims at developing deep learning models with built-in robustness to data domain shift that also offers a high-level explainability.


Further reading

Detection of sea mammals from aerial imagery
December 18, 2020
Better solutions are needed to estimate the populations of sea mammals, such as breeding seals, from aerial images of the sea ice.
Performing objective measurements in ultrasound images
March 8, 2021
Exploiting limited data to perform objective measurements in ultrasound images of the heart.
Oil-spill detection and characterization of thickness
December 18, 2020
Visual Intelligence collaborates with KSAT to develop new models for detecting and characterizing oil spills.