1. Introduction
Capsule Networks (CapsNets) offer an intriguing alternative to Convolutional Neural Networks, particularly for their ability to preserve spatial hierarchies and relationships in data. However, CapsNets, especially their primary capsule layers, often suffer from high computational cost and parameter redundancy, limiting their practical deployment. This work addresses these challenges by introducing PrunedCaps, a method focused on efficient discrimination and pruning of primary capsules. The article primarily utilizes the Capsule Network (CapsNet) architecture as proposed by Sabour et al., enhanced with novel pruning mechanisms and a discrimination module designed to identify and reduce redundant primary capsules.
2. Related Work
Research on Capsule Networks has explored various improvements in routing algorithms, architectural modifications, and applications since their inception. Concurrently, model pruning has emerged as a critical technique for deploying deep neural networks on resource-constrained devices, with methods ranging from magnitude-based pruning to structured and dynamic approaches. While many pruning techniques focus on convolutional layers or fully connected layers, there is limited work specifically targeting the primary capsule layer of CapsNets. This paper differentiates itself by proposing a discrimination-aware pruning strategy tailored for this unique architectural component.
3. Methodology
The PrunedCaps methodology involves a two-stage process: first, a discrimination module evaluates the contribution and importance of individual primary capsules, and second, an iterative pruning strategy removes the least significant capsules. The discrimination module leverages activation patterns and routing agreement scores to quantify a capsule's utility in representation learning. Based on these scores, a thresholding or ranking mechanism is employed to identify candidates for pruning, followed by fine-tuning the remaining network. This ensures that only relevant and discriminative primary capsules are retained, optimizing the network's structure.
4. Experimental Results
Experiments conducted on benchmark datasets, such as MNIST and Fashion-MNIST, demonstrate the effectiveness of PrunedCaps. The proposed method achieves competitive classification accuracy compared to the original CapsNet while significantly reducing the number of primary capsules and overall model parameters. For instance, on MNIST, PrunedCaps maintained over 99% accuracy with a 30% reduction in primary capsules. The results below illustrate the performance benefits and efficiency gains across various metrics, showcasing PrunedCaps' ability to compress models without substantial performance degradation. The table shows a comparison of PrunedCaps against the baseline CapsNet model across key performance and efficiency metrics on a standard dataset.
5. Discussion
The findings confirm that targeted discrimination and pruning of primary capsules offer a viable path to developing more efficient and compact Capsule Networks. PrunedCaps not only reduces model size and inference time but also potentially enhances interpretability by focusing on more discriminative features. While the current implementation focuses on primary capsules, future work could explore extending discrimination and pruning to dynamic routing mechanisms or higher-level capsules. This approach paves the way for broader adoption of CapsNets in real-world applications where computational resources are a constraint.