ProtoCaps presents a non-iterative routing method for Capsule Networks to address computational challenges. The approach reduces memory requisites during training and demonstrates superior results compared to existing methods. By introducing trainable prototype clustering, ProtoCaps aims to enhance operational efficiency and performance in complex computational scenarios.
Capsule Networks overcome CNN shortcomings by building part-whole relationships using Capsules. Iterative routing mechanisms in Capsule Networks pose scalability issues due to high computational complexity. ProtoCaps offers a solution with shared subspace projection, reducing memory requirements and improving efficiency.
The paper compares ProtoCaps with other Capsule Network routing algorithms on various datasets, showcasing its effectiveness. Ablation studies reveal the robustness of ProtoCaps across multiple datasets and suggest potential architectural refinements for further enhancement.
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by Miles Everet... at arxiv.org 03-11-2024
https://arxiv.org/pdf/2307.09944.pdfDeeper Inquiries