通过综合对比学习和两阶段训练实现可解释的无监督胶囊网络

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-09-30 DOI:10.1016/j.patcog.2024.111059
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引用次数: 0

摘要

由于协调学习可解释的初级胶囊和高级胶囊所面临的挑战,人们对具有对比学习功能的无监督胶囊网络(CapsNets)关注有限。为了解决这个问题,我们重点关注三个方面:损失函数、路由算法和训练策略。首先,我们提出了一个全面的对比损失函数,以确保在学习不同对象的高级和初级胶囊时的一致性。其次,我们引入了一种基于协议的路由机制,用于激活高级胶囊。最后,我们提出了一种两阶段训练策略,以解决多重损失之间的冲突。消融实验表明,这些方法都能提高模型性能。线性评估和半监督学习的结果表明,我们的模型在学习高级胶囊方面优于其他 CapsNets 和卷积神经网络。此外,通过对胶囊进行可视化,还能深入了解主要胶囊,这些胶囊在不同图像中保持一致,并与人类视觉相吻合。
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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 strategy. First, we propose a comprehensive contrastive loss to ensure consistency in learning both high-level and primary capsules across different objects. Next, we introduce an agreement-based routing mechanism for the activation of high-level capsules. Finally, we present a two-stage training strategy to resolve conflicts between multiple losses. Ablation experiments show that these methods all improve model performance. Results from linear evaluation and semi-supervised learning demonstrate that our model outperforms other CapsNets and convolutional neural networks in learning high-level capsules. Additionally, visualizing capsules provides insights into the primary capsules, which remain consistent across images and align with human vision.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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