Michael Kampffmeyer

Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

Mar 16, 2023
Luca Tomasetti, Stine Hansen, Mahdieh Khanmohammadi, Kjersti Engan, Liv Jorunn Høllesli, Kathinka Dæhli Kurz, Michael Kampffmeyer

Figure 2 for Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

Figure 3 for Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

Figure 4 for Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

Access Paper or Ask Questions

Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning

Nov 25, 2022
Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer, Qingling Cai, Xiaodan Liang

Figure 2 for Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning

Figure 3 for Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning

Figure 4 for Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning

* 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Access Paper or Ask Questions

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

Oct 15, 2022
Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina MC Höhne, Michael Kampffmeyer

Figure 2 for ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

Figure 3 for ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

Figure 4 for ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

Access Paper or Ask Questions

PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on

Jul 27, 2022
Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xin Dong, Feida Zhu, Xiaodan Liang

Figure 2 for PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on

Figure 3 for PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on

Figure 4 for PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on

* arXiv admin note: substantial text overlap with arXiv:2111.10544

Access Paper or Ask Questions

Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

Jun 30, 2022
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu

Figure 2 for Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

Figure 3 for Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

Figure 4 for Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

* Accepted by MICCAI 2022

Access Paper or Ask Questions

The Kernelized Taylor Diagram

May 18, 2022
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen

Figure 2 for The Kernelized Taylor Diagram

* Accepted at the Norwegian Artificial Intelligence Symposium 2022. Code available at: https://github.com/Wickstrom/KernelizedTaylorDiagram

Access Paper or Ask Questions

Automatic Identification of Chemical Moieties

Mar 30, 2022
Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke

Figure 2 for Automatic Identification of Chemical Moieties

Figure 3 for Automatic Identification of Chemical Moieties

Figure 4 for Automatic Identification of Chemical Moieties

Access Paper or Ask Questions

Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

Mar 17, 2022
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen

Figure 2 for Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

Figure 3 for Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

Figure 4 for Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series

* Published in Journal of Pattern Recognition Letters: https://www.sciencedirect.com/science/article/pii/S0167865522000502 Code available at: https://github.com/Wickstrom/MixupContrastiveLearning

Access Paper or Ask Questions

Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

Mar 03, 2022
Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer

Figure 2 for Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

Figure 3 for Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

Figure 4 for Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

* Accepted in Medical Image Analysis

Access Paper or Ask Questions