164 citations · 413 across the 15 of their papers we have counts for
8 papers · 1 filter
Joint Optimization of an Autoencoder for Clustering and Embedding
Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen +1
Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternat…
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer +2
Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often a…
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance. However, how to do this effici…
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we…
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Co…
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Luigi T. Luppino, Mads A. Hansen, Michael Kampffmeyer +4
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection in bitemporal satellite images. A main challenge is the align…