DeepCaps: Going Deeper with Capsule Networks
Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Suranga Seneviratne, Ranga Rodrigo
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- github.com/brjathu/deepcapsOfficialIn papertf★ 0
- github.com/mfarhadi98/Use-Capsule-Networks-for-kddcuptf★ 0
- github.com/Ugenteraan/DeepCapspytorch★ 0
- github.com/HopefulRational/DeepCaps-PyTorchpytorch★ 0
- github.com/Ugenteraan/Deep-CapsNetpytorch★ 0
Abstract
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 DeepCaps1, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain 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.