• 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 ...
    • A clinically motivated self-supervised approach for content-based image retrieval of CT liver images 

      Wickstrøm, Kristoffer; Østmo, Eirik Agnalt; Radiya, Keyur; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2023-05-09)
      Deep learning-based approaches for content-based image retrieval (CBIR)of computed tomography (CT) liver images is an active field of research but suffer from some critical limitations. First,they are heavily reliant on ...
    • Deep generative models for reject inference in credit scoring 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2020)
      Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of ...
    • Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data 

      Choi, Changkyu; Kampffmeyer, Michael; Jenssen, Robert; Handegard, Nils Olav; Salberg, Arnt-Børre (Journal article; Peer reviewed, 2023)
      Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. ...
    • Dense dilated convolutions merging network for land cover classification 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Journal article; Peer reviewed, 2020)
      Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the ...
    • Explaining decisions of deep neural networks used for fish age prediction 

      Ordonez, Alba; Eikvil, Line; Salberg, Arnt-Børre; Harbitz, Alf; Murray, Sean Meling; Kampffmeyer, Michael (Peer reviewed; Journal article, 2020)
    • Generating customer's credit behavior with deep generative models 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2022)
      Innovation is considered essential for today's organizations to survive and thrive. Researchers have also stressed the importance of leadership as a driver of followers' innovative work behavior (FIB). Yet, despite a large ...
    • 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 ...
    • Learning latent representations of bank customers with the Variational Autoencoder 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2020)
      Learning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In ...
    • Machine Learning + Marine Science: Critical Role of Partnerships in Norway 

      Handegard, Nils Olav; Eikvil, Line; Jenssen, Robert; Kampffmeyer, Michael; Salberg, Arnt Børre; Malde, Ketil (Others, 2021)
      In this essay, we review some recent advances in developing machine learning (ML) methods for marine science applications in Norway. We focus mostly on deep learning (DL) methods and review the challenges we have faced in ...
    • Mixing up contrastive learning: Self-supervised representation learning for time series 

      Wickstrøm, Kristoffer; Kampffmeyer, Michael; Mikalsen, Karl Øyvind; Jenssen, Robert (Journal article; Peer reviewed, 2022)
    • Mixing up contrastive learning: Self-supervised representation learning for time series 

      Kampffmeyer, Michael; Mikalsen, Karl Øyvind; Jenssen, Robert (Journal article; Peer reviewed, 2022)
    • Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Chapter, 2020)
      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-Constructing Graph (SCG) module, which makes ...
    • 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 ...
    • Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective 

      Trosten, Daniel Johansen; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2021)
    • Self-Constructing Graph Convolutional Networks for Semantic Labeling 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt-Børre (Chapter, 2020)
      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 propose a novel architecture called the ...
    • Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Journal article; Peer reviewed, 2021)
      Capturing global contextual representations in remote sensing images by exploiting long-range pixel-pixel dependencies has been shown to improve segmentation performance. However, how to do this efficiently is an open ...
    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Journal article; Peer reviewed, 2021)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...
    • The 1st Agriculture-Vision Challenge: Methods and Results 

      Chiu, Mang Tik; Xingqiang, Xu; Wang, Kai; Hobbs, Jennifer; Hovakimyan, Naira; Huang, Thomas S.; Shi, Honghui; Wei, Yunchao; Huang, Zilong; Schwing, Alexander; Brunner, Robert; Dozier, Ivan; Dozier, Wyatt; Ghandilyan, Karen; Wilson, David; Park, Hyunseong; Kim, Junhee; Kim, Sungho; Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre; Barbosa, Alexandre; Trevisan, Rodrigo; Zhao, Bingchen; Yu, Shaozuo; Yang, Siwei; Wang, Yin; Sheng, Hao; Chen, Xiao; Su, Jingyi; Rajagopal, Ram; Ng, Andrew; Huynh, Van Thong; Kim, Soo-Hyung; Na, In-Seop; Baid, Ujjwal; Innani, Shubham; Dutande, Prasad; Baheti, Bhakti; Talbar, Sanjay; Tang, Jianyu (Chapter, 2020)