Summary of Mitigating Vanishing Activations in Deep Capsnets Using Channel Pruning, by Siddharth Sahu and Abdulrahman Altahhan
Mitigating Vanishing Activations in Deep CapsNets Using Channel Pruning
by Siddharth Sahu, Abdulrahman Altahhan
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Capsule Networks have been shown to excel at learning part-whole relationships while maintaining viewpoint invariance, thanks to their multidimensional capsules. However, recent studies have revealed that Capsule Networks suffer from a lack of scalability due to vanishing activations in deeper layers. This paper delves into the vanishing activation problem in deep Capsule Networks and investigates how increasing capsule dimensions can facilitate deeper networks. To tackle this issue, various Capsule Network models are constructed and evaluated with different numbers of capsules, capsule dimensions, and intermediate layers. Interestingly, this study employs a novel approach by pruning the backbone network and capsule layers to reduce inactive capsules and improve model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been studying how computers can understand objects from different angles without getting confused. They found that a special type of computer program called Capsule Networks is really good at this job. But there’s a problem – these networks can’t be made bigger to learn more complex things. This study tries to fix this issue by making the networks stronger and more efficient. To do this, they test different versions of the network with varying amounts of information and complexity. They even came up with a new way to make the networks better by removing unnecessary parts. |
Keywords
» Artificial intelligence » Pruning
