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Dokumenttyp:
Zeitschriftenaufsatz 
Autor(en):
Liebel, Lukas; Körner, Marco 
Titel:
Auxiliary Tasks in Multi-task Learning 
Abstract:
Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expecte...    »
 
Stichworte:
Computer Vision, without_full_paper_review, multi-task, arxiv 
Zeitschriftentitel:
arXiv preprint arXiv:1805.06334 
Jahr:
2018 
Reviewed:
nein 
Sprache:
en 
Status:
Preprint / submitted