Introducing Routing Uncertainty in Capsule Networks

被引:0
|
作者
Ribeiro, Fabio De Sousa [1 ]
Leontidis, Georgios [2 ]
Kollias, Stefanos [1 ]
机构
[1] Univ Lincoln, Machine Learning Grp, Lincoln, England
[2] Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020 | 2020年 / 33卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment. In our formulation, the local routing iterations are replaced with variational inference of part-object connections in a probabilistic capsule network, leading to a significant speedup without sacrificing performance. In this way, global context is also considered when routing capsules by introducing global latent variables that have direct influence on the objective function, and are updated discriminatively in accordance with the minimum description length (MDL) principle. We focus on enhancing capsule network properties, and perform a thorough evaluation on pose-aware tasks, observing improvements in performance over previous approaches whilst being more computationally efficient.
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页数:13
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