Daniel J. Trosten

Recurrent Deep Divergence-based Clustering for Simultaneous Feature Learning and Clustering of Variable Length Time Series

Michael Kampffmeyer, Robert Jenssen, Andreas Storvik Strauman, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives pdf

Robert Jenssen, Michael C. Kampffmeyer, Daniel J. Trosten, 2019, ArXiv.

Unsupervised Feature Extraction - A CNN-Based Approach

Puneet Sharma, Daniel J. Trosten, P. Sharma, 2019, SCIA.

RELAX: Representation Learning Explainability pdf

Robert Jenssen, Daniel J. Trosten, Kristoffer K. Wickstrom, 2021, ArXiv.

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

Michael Kampffmeyer, Robert Jenssen, Daniel J. Trosten, 2021 .

Reconsidering Representation Alignment for Multi-view Clustering

Michael Kampffmeyer, Robert Jenssen, Daniel J. Trosten, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

RELAX: Representation Learning Explainability

Karl Øyvind Mikalsen, Daniel J. Trosten, Michael C. Kampffmeyer, 2021, International Journal of Computer Vision.

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings

Daniel J. Trosten, Michael C. Kampffmeyer, R. Jenssen, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

Daniel J. Trosten, Michael C. Kampffmeyer, R. Jenssen, 2023, Computer Vision and Pattern Recognition.

Pattern Recognition

Jesús Ariel Carrasco-Ochoa, Ching Y. Suen, José Francisco Martínez-Trinidad, 2014, Lecture Notes in Computer Science.