Encapsulating Capsule Networks: Everything You Need To Know
This Tutorial and blog post covers everything from intuition to the basics to a complicated distribution routing algorithm. As long as you don’t have a medical phobia of capsules, you’ll be able to swallow all the knowledge in this post.
Photo Link When applying Convolutional Neural Networks (CNNs) (LeCun et al., 1990) to a computer vision task, a change in viewpoint (change in orientation, position, shear, etc.) is likely to lead to drastically different network activations, hindering the model’s ability to generalize. To solve this problem, current CNNs require a large number of parameters, datasets and computational power. This lead to the introduction of Capsule Networks (Hinton et al., 2011) . Capsule Networks aim to generalize to different viewpoints by taking advantage of the fact that the relationship between parts of
Explore this link on the map →related reading
- Capsule neural network - Wikipediaen.wikipedia.org
- Are capsules a good idea? A generative perspective - OATMLoatml.cs.ox.ac.uk
- Zoom In: An Introduction to Circuitsdistill.pub
- Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
- Neural Networks, Manifolds, and Topology -- colah's blogcolah.github.io
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- Feature Visualizationdistill.pub
- Bayesian Neural Networkscs.toronto.edu
- arxiv.org/pdf/2511.08544arxiv.org
- The Little Book of Deep Learningfleuret.org
- Naturally Occurring Equivariance in Neural Networksdistill.pub
- Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com