Scientific publications

At Visual Intelligence we work across our innovation areas to extract knowledge from large volumes of visual data more efficiently through automatic and intelligent data analysis. The work to address the core research challenges in deep learning: working with limited training data, utilizing context and dependencies, providing explainability, confidence and uncertainty, are important in all the innovation areas.

publications as of January 2021:
March 13, 2021
March 17, 2021
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer

Reconsidering Representation Alignment for Multi-view Clustering

To appear in CVPR 2021.

We identify several drawbacks with current state of the art methods for deep multi-view clustering, and present a simple baseline model that avoids these drawbacks. The baseline is expanded with a contrastive learning component, resulting in a model which outperforms the current SOTA on several benchmark datasets.

February 19, 2021
Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, Jose Principe

Measuring Dependence with Matrix‐Based Entropy Functional

Published to AAAI-21.

An interpretable and differentiable dependence (or independence) measure that can be used to 1) train deep network under covariate shift and non-Gaussian noise; 2) implement a deep deterministic information bottleneck; and 3) understand the dynamics of learning of CNN. Code available.