تقرير
Examining the Benefits of Capsule Neural Networks
العنوان: | Examining the Benefits of Capsule Neural Networks |
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المؤلفون: | Punjabi, Arjun, Schmid, Jonas, Katsaggelos, Aggelos K. |
سنة النشر: | 2020 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning |
الوصف: | Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks. By replacing the standard scalar activations with vectors, and by connecting the artificial neurons in a new way, capsule networks aim to be the next great development for computer vision applications. However, in order to determine whether these networks truly operate differently than traditional networks, one must look at the differences in the capsule features. To this end, we perform several analyses with the purpose of elucidating capsule features and determining whether they perform as described in the initial publication. First, we perform a deep visualization analysis to visually compare capsule features and convolutional neural network features. Then, we look at the ability for capsule features to encode information across the vector components and address what changes in the capsule architecture provides the most benefit. Finally, we look at how well the capsule features are able to encode instantiation parameters of class objects via visual transformations. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2001.10964 |
رقم الأكسشن: | edsarx.2001.10964 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |