An interpretable unsupervised capsule network via comprehensive contrastive learning and two-stage training

Limited attention has been given to unsupervised capsule networks (CapsNets) with contrastive learning due to the challenge of harmoniously learning interpretable primary and high-level capsules. To address this issue, we focus on three aspects: loss function, routing algorithm, and training strateg...

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Veröffentlicht in:	Pattern recognition Jg. 158; S. 111059
Hauptverfasser:	Zeng, Ru, Song, Yan, Zhong, Yanjiu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2025
Schlagworte:	Comprehensive contrastive loss Interpretability Primary capsules Routing algorithm Unsupervised capsule network Unsupervised capsule network Routing algorithm Comprehensive contrastive loss Interpretability Primary capsules
ISSN:	0031-3203
Online-Zugang:	Volltext
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