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Simon Vandenhende
Researcher at Katholieke Universiteit Leuven
Publications - 27
Citations - 1182
Simon Vandenhende is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Task (project management) & Computer science. The author has an hindex of 10, co-authored 20 publications receiving 399 citations.
Papers
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Journal ArticleDOI
Multi-Task Learning for Dense Prediction Tasks: A Survey.
Simon Vandenhende,Stamatios Georgoulis,Wouter Van Gansbeke,Marc Proesmans,Dengxin Dai,Luc Van Gool +5 more
TL;DR: This survey provides a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision, explicitly emphasizing on dense prediction tasks.
Posted Content
SCAN: Learning to Classify Images without Labels
TL;DR: This paper deviates from recent works, and advocate a two-step approach where feature learning and clustering are decoupled, and achieves promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime without the use of any ground-truth annotations.
Book ChapterDOI
SCAN: Learning to Classify Images Without Labels
TL;DR: Wang et al. as mentioned in this paper proposed a two-step approach where feature learning and clustering are decoupled, and obtained semantically meaningful features as a prior in a learnable clustering approach.
Book ChapterDOI
MTI-Net: Multi-scale Task Interaction Networks for Multi-task Learning
TL;DR: This paper argues about the importance of considering task interactions at multiple scales when distilling task information in a multi-task learning setup, and proposes a novel architecture, namely MTI-Net, that builds upon this finding in three ways.
Posted Content
Branched Multi-Task Networks: Deciding What Layers To Share.
TL;DR: This paper proposes an approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities, given a specific budget, and generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.