Capsule Network Performance on Complex Data
Edgar Xi, Selina Bing, Yang Jin

TL;DR
This paper evaluates capsule networks' ability to handle complex data, focusing on their rotational invariance and spatial awareness, and compares their performance to CNNs on datasets like CIFAR10.
Contribution
It explores optimal configurations of capsule networks for higher-dimensional data, demonstrating their advantages over traditional CNNs in complex classification tasks.
Findings
Capsule networks achieve lower test error on CIFAR10 compared to CNNs.
Capsule networks demonstrate rotational invariance and spatial awareness.
Optimal configurations improve performance on complex datasets.
Abstract
In recent years, convolutional neural networks (CNN) have played an important role in the field of deep learning. Variants of CNN's have proven to be very successful in classification tasks across different domains. However, there are two big drawbacks to CNN's: their failure to take into account of important spatial hierarchies between features, and their lack of rotational invariance. As long as certain key features of an object are present in the test data, CNN's classify the test data as the object, disregarding features' relative spatial orientation to each other. This causes false positives. The lack of rotational invariance in CNN's would cause the network to incorrectly assign the object another label, causing false negatives. To address this concern, Hinton et al. propose a novel type of neural network using the concept of capsules in a recent paper. With the use of dynamic…
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Taxonomy
TopicsAdvanced Neural Network Applications · Domain Adaptation and Few-Shot Learning · Advanced Image and Video Retrieval Techniques
MethodsCapsule Network
