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.

Featured blog posts

AI matches human experts in classifying microscopic organisms

July 16, 2025
By
Iver Martinsen, Steffen Aagaard Sørensen, Samuel Ortega, Fred Godtliebsen, Miguel Tejedor, Eirik Myrvoll-Nilsen

Visual Data Diagnosis and Debiasing with Concept Graphs

September 26, 2024
By
Chakraborty, Rwiddhi; Wang, Yinong; Gao, Jialu; Zheng, Runkai; Zhang, Cheng; De la Torre, Fernando

All publications

Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective

By authors:

Daniel J. Trosten, Robert Jenssen, and Michael C. Kampffmeyer

Published in:

Vol. 2 (2021): Proceedings of the Northern Lights Deep Learning Workshop 2021

on

September 11, 2021

Self-supervised Multi-task Representation Learning for Sequential Medical Images

By authors:

Dong, N., Kampffmeyer, M., Voiculescu, I.

Published in:

Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12977. Springer, Cham

on

September 11, 2021

Semi-supervised target classification in multi-frequency echosounder data

By authors:

Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Olav Brautaset, Line Eikvil, Robert Jenssen

Published in:

ICES Journal of Marine Science, Volume 78, Issue 7, October 2021, Pages 2615–2627

on

August 12, 2021

Instance Segmentation of Microscopic Foraminifera

By authors:

Johansen, Thomas Haugland; Sørensen, Steffen Aagaard; Møllersen, Kajsa; Godtliebsen, Fred

Published in:

Applied Sciences 2021 ;Volum 11.(14)

on

July 16, 2021

Joint optimization of an autoencoder for clustering and embedding

By authors:

Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen

Published in:

Machine Learning (2021)

on

June 21, 2021

Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images

By authors:

Qinghui Liu (Brian), Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

Published in:

International Journal of Remote Sensing

on

June 16, 2021

RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks

By authors:

Ron Levie , Çagkan Yapar , Gitta Kutyniok, and Giuseppe Caire

Published in:

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 20, NO. 6, JUNE 2021

on

June 6, 2021

Reconsidering Representation Alignment for Multi-view Clustering

By authors:

Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer

Published in:

CVPR 2021

on

March 13, 2021

Morphological and molecular breast cancer profiling through explainable machine learning

By authors:

Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller & Frederick Klauschen

Published in:

Nature Machine Intelligence volume 3, pages 355–366 (2021)

on

March 8, 2021

Pruning by explaining: A novel criterion for deep neural network prunin

By authors:

Yeom, Seul-Ki; Seegerer, Philipp; Lapuschkin, Sebastian; Binder, Alexander; Wiedemann, Simon; Müller, Klaus-Robert; Samek, Wojciech.

Published in:

Pattern Recognition

on

March 3, 2021

Other publications

annual reports