Geodesy

Routing (electronic design automation)

Benchmark (surveying)

Network architecture

Embedded system

Static routing

Computer security

Regularization (linguistics)

Computer science

Geology

Computer network

Paleontology

Routing protocol

MNIST database

Geography

Adaptive routing

Bottleneck

Artificial intelligence

Capsule

Deep learning

Artificial neural network

Convolutional neural network

Machine learning

Key (lock)

Convolution (computer science)

Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art capsule domain networks results on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

DeepCaps: Going Deeper With Capsule Networks