gram-ai/capsule-networks
A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".
Implements the iterative routing-by-agreement mechanism where lower-level capsules route outputs to higher-level capsules based on prediction agreement, enabling the network to learn instantiation parameters (pose, scale, rotation) rather than just class probabilities. Integrates with TorchNet for training, TorchVision for MNIST preprocessing, and Visdom for real-time visualization, achieving 99.7% accuracy on MNIST with configurable routing iterations and batch sizes.
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Nov 09, 2018
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