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

Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice

By authors:

Kuttner, Samuel; Luppino, Luigi Tommaso; Convert, Laurence; Sarrhini, Otman; Lecomte, Roger; Kampffmeyer, Michael Christian; Sundset, Rune; Jenssen, Robert.

Published in:

Frontiers in Nuclear Medicine

on

April 11, 2024

PTUS: Photo-Realistic Talking Upper-Body Synthesis via 3D-Aware Motion Decomposition Warping

By authors:

Lin, Luoyang; Jiang, Zutao; Liang, Xiaodan; Ma, Liqian; Kampffmeyer, Michael Christian; Cao, Xiaochun.

Published in:

Proceedings of the AAAI Conference on Artificial Intelligence, 38(4), 3441-3449

on

March 24, 2024

ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations

By authors:

Chakraborty, Rwiddhi; Sletten, Adrian; Kampffmeyer, Michael Christian.

Published in:

Computer Vision and Pattern Recognition 2024

on

March 20, 2024

Defending Against Poisoning Attacks in Federated Learning with Blockchain

By authors:

Dong, Nanqing; Wang, Zhipeng; Sun, Jiahao; Kampffmeyer, Michael Christian; Knottenbelt, William; Xing, Eric.

Published in:

IEEE Transactions on Artificial Intelligence (TAI)

on

March 18, 2024

Interrogating Sea Ice Predictability With Gradients

By authors:

Joakimsen, H. L., Martinsen I., Luppino, L. T., McDonald, A., Hosking, S., and Jenssen, R.

Published in:

IEEE Geoscience and Remote Sensing Letters

on

February 14, 2024

Mixed Nash for Robust Federated Learning

By authors:

Xie, Wanyun; Pethick, Thomas; Ramezani-Kebrya, Ali; Cevher, Volkan

Published in:

Transactions on Machine Learning Research (02/2024)

on

February 4, 2024

Cauchy-Schwarz Divergence Information Bottleneck for Regression

By authors:

Yu, Shujian; Løkse, Sigurd Eivindson; Jenssen, Robert; Principe, Jose.

Published in:

International Conference on Learning Representations 2024

on

January 16, 2024

A self-supervised inspired object scoring system for building change detection

By authors:

Jensen, Are Charles

Published in:

Proceedings of Machine Learning Research (PMLR) ISSN 2640-3498. 233, p. 97–103

on

January 8, 2024

On the Generalization of Stochastic Gradient Descent with Momentum

By authors:

Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang

Published in:

Journal of Machine Learning Research 25 (2024) 1-56

on

January 1, 2024

Discriminative multimodal learning via conditional priors in generative models

By authors:

Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen,

Published in:

Neural Networks, Volume 169, 2024, Pages 417-430

on

November 4, 2023

Other publications

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