CapsNet-pytorch and capsule-networks
These are **competitors** — both are independent PyTorch implementations of the same foundational capsule network architecture from the same paper, and users would typically choose one based on code quality, documentation, or community preference rather than use them together.
About CapsNet-pytorch
adambielski/CapsNet-pytorch
PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
This project offers a PyTorch implementation for the 'Dynamic Routing Between Capsules' neural network architecture, enabling researchers to explore advanced image recognition. It takes raw image data, like the MNIST dataset, and outputs classifications and detailed visualizations of how the network 'sees' and reconstructs digits. Machine learning researchers and academics specializing in computer vision will find this useful for experimenting with capsule networks.
About capsule-networks
gram-ai/capsule-networks
A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".
This project offers a PyTorch implementation of Capsule Networks, a neural network architecture particularly effective for image recognition. It takes an image as input and outputs a classification of the objects or parts within it, even when they overlap. This would be used by a machine learning engineer or researcher experimenting with advanced image classification models beyond standard convolutional neural networks.
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