PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
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Updated
Apr 13, 2021 - Python
PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
A tensorflow implementation of Hinton's [matrix capsules with EM routing](https://openreview.net/pdf?id=HJWLfGWRb)
A TensorFlow implementation of "Matrix Capsules with EM Routing" by Hinton et al. (2018).
A PyTorch Implementation of Matrix Capsules with EM Routing
Reference implementation of "An Algorithm for Routing Vectors in Sequences" (Heinsen, 2022) and "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019), for composing deep neural networks.
A tensorflow implementation for CapsNet
The code for "No Routing Needed Between Capsules". This repository contains the code used for the experiments detailed in a forthcoming paper. The paper is available pre-published at arXiv: http://arxiv.org/abs/2001.09136
Another implementation of Hinton's capsule networks in tensorflow.
Stacked Capsule Autoencoders (SCAE) in PyTorch and their semantic interpretation
Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks. This repository contains the code used for the experiments detailed in a paper currently submitted to IEEE Transactions on Neural Networks and Learning Systems. The paper is available pre-published at arXiv: http://arxiv.org/abs/1906.08676
A lightweight, human-scale, extensible content framework for the small web
A tensorflow implemention of CapsNet in Geoffrey Hinton's paper Dynamic Routing Between Capsules
Presentation for paper "Single Image Super-Resolution Based on Capsule Neural Networks"
☕ Brewing coffee pods without the hassle of complex machinery.
easy definition of tensor flow based neural networks
Matrix Capsules experiment on German Traffic Sign Recognition Benchmark (GTSRB)
🚀🔍 Search platform for SpaceX complex physical items
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