Recently, Geoffrey Hinton and his colleagues made the article about capsules available. He has been known to heavily criticize the use of pooling and back propagation.
“A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part.” The nodes of inputs and outputs are vectors, instead of scalars as in neural networks. A cheat sheet comparing the traditional neurons and capsules is as follow:
Based on the capsule, the authors suggested a new type of layer called CapsNet.
Huadong Liao implemented CapsNet with TensorFlow according to the paper. (Refer to his repository.)
- Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, “Dynamic Routing Between Capsules,” arXiv:1710.09829 (2017). [arXiv]
- “浅析 Hinton 最近提出的 Capsule 计划” (2017). [Zhihu] (in Chinese)
- “如何看待Hinton的论文《Dynamic Routing Between Capsules》？” (2017) [Zhihu] (in Chinese)
- Github: naturomics/CapsNet-Tensorflow [Github]
- Nick Bourdakos, “Capsule Networks Are Shaking up AI — Here’s How to Use Them,” Medium (2017). [Medium]