Explainability and reliability


When black-box algorithms like deep neural networks are making decisions that were previously entrusted to humans, it becomes more and more necessary for these algorithms to explain themselves.


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. There is therefore a need for explainability, which means that the models should be able to summarize the reasons for their predictions, both to gain the trust of users and to produce insights about the causes of their decisions.

Main objective

To open the "black box" of deep learning in order to develop explainable and reliable prediction models.

Highlighted publications

Addressing Distribution Shifts in Federated Learning for Enhanced Generalization Performance
June 4, 2023
Training and test data from different clients pose a challenge.
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
March 6, 2023
We approach the representation learning task by tackling the hubness problem.
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
March 6, 2023
We propose DeepMVC – a unified framework which includes many recent methods as instances.