lmcad-unicamp / lanes-capsnet

Separable and resource efficient organization of Capsule Networks (CapsNet)

The Multi-lane CapsNet (MLCN)

We introduce Multi-Lane Capsule Networks (MLCN), which are a separable and resource efficient organization of Capsule Networks (CapsNet) that allows parallel processing while achieving high accuracy at reduced cost. A MLCN is composed of a number of (distinct) parallel lanes, each contributing to a dimension of the result, trained using the routing-by-agreement organization of CapsNet. Our results indicate similar accuracy with a much-reduced cost in number of parameters for the Fashion-MNIST and Cifar10 datsets. They also indicate that the MLCN outperforms the original CapsNet when using a proposed novel configuration for the lanes. MLCN also has faster training and inference times, being more than two-fold faster than the original CapsNet in the same accelerator.

Full paper: https://arxiv.org/abs/1902.08431

Source Code

This MLCN implementation used the @XifengGuo CapsNet Keras implementation (https://github.com/XifengGuo/CapsNet-Keras) as its base. All source code is available with the MIT license.

How to use it

Some of the details of how to install and use it can be found in the CapsNet-Keras project README. To support multi-lanes we introduced some new command line arguments:

• --lane_size size of the lanes (an integer that should be greater or equal to one)
• --lane_type type of the lanes (1 to mlcn1 and 2 for mlcn2)
• --num_lanes number of lanes (an integer that should be greater or equal to 2)
• --dataset mnist or cifar10 dataset
• --dropout percentage of lanes being dropped out per batch