Blar i NR vitenarkiv på forfatter "Løkse, Sigurd Eivindson"

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    • Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy 

      Wickstrøm, Kristoffer; Løkse, Sigurd Eivindson; Kampffmeyer, Michael; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Peer reviewed, 2023)
      Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs’ generalization ability. However, it is by no means obvious how to ...

    • Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy 

      Wickstrøm, Kristoffer Knutsen; Løkse, Sigurd Eivindson; Kampffmeyer, Michael Christian; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Peer reviewed, 2023)

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

      Trosten, Daniel Johansen; Chakraborty, Rwiddhi; Løkse, Sigurd Eivindson; Wickstrøm, Kristoffer; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)
      Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where ...

    • The Kernelized Taylor Diagram 

      Wickstrøm, Kristoffer; Johnson, Juan Emmanuel; Løkse, Sigurd Eivindson; Camps-Valls, Gusatu; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2022)
      This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to ...

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

      Trosten, Daniel Johansen; Løkse, Sigurd Eivindson; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)
      Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially ...