The Multi-Lane Capsule Network

Rosário, Vanderson Martins do; Borin, Edson; Breternitz, Maurício

doi:10.1109/lsp.2019.2915661

Cited by 48 publications

(16 citation statements)

References 7 publications

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“…The HitNet [10], the network proposed by Zhao et al [13] and the Mlcn2 [12], these models improve network performance by modifying routing algorithms mentioned in section II. However, as the table 1 shows, the methods that only modifies the routing is not as effective as the method that deepens the depth, such as DeepCaps [5] and ours.…”

Section: B Classification Resultsmentioning

confidence: 99%

“…Peer et al [7] poof a drawback in dynamic routing algorithms that is incapable of distinguishing symmetric inputs, and propose a reasonable solution: bias and homogeneous representation of instantiation vectors. Do Rosario et al [12] propose a novel model that is called multi-lane capsule network, which is similar to a multi-network integration solution. Zhao et al [13] use the scale-invariant Max-Min function improves the performance of CapsNet during the routing process.…”

Section: Related Workmentioning

confidence: 99%

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Deep Tensor Capsule Network

Sun

Yuan

et al. 2020

IEEE Access

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Capsule network is a promising model in computer vision. It has achieved excellent results on simple datasets such as MNIST, but the performance deteriorates as data becomes complicated. In order to address this issue, we propose a deep capsule network in this paper. To deepen the capsule network, we present a new tensor capsule based routing algorithm and the corresponding convolution operation. Compared to vector capsules, tensor capsules can capture more instance-level information. Together, the relevant convolution operation is beneficial for reducing the amount of parameters in the routing process. Furthermore, we propose a dropout mechanism for vectors and tensors in order to alleviate the potential overfitting problem. Finally, we also inject the multi-scale capsules of the middle layers into a multi-scale decoder to pursue more details of an image and more clear reconstructed image. Experimental results on CIFAR10, Fashion-MNIST, and SVHN demonstrate that the proposed deep tensor network can achieve very competitive performance compared to other state-of-the-art capsule networks.

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Section: B Classification Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Tensor Capsule Network

Sun

Yuan

et al. 2020

IEEE Access

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“…In the capsule layer, the primary capsules are processed by some methods, such as pruning [26] or merging [27], to reduce the calculation cost of routing and accelerate capsule processing [28]. In addition, the representation ability of capsules also promoted by the attention modules [29], the space promotion module [30], and several methods of interaction between capsules [31].…”

Section: A Capsule Networkmentioning

confidence: 99%

An Improved Capsule Network Based on Capsule Filter Routing

et al. 2021

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Capsule network (CapsNet) is a novel type of network that can retain spatial information, because each capsule can integrate more information than scalar-output features. However, the CapsNet learns all the features in the input image due to the lack of pooling operation, and there is no connection between different layers in the multi-layer network structure. In this paper, we propose an improved capsule network (CapsNet) based on Capsule Filter Routing (CFR) to address this problem, namely CFR-CapsNet. Firstly, we propose a new routing method called CFR for filtering capsules based on capsule activation values, which can speed up the operation of the model, and then introduce a self-attention mechanism to improve the performance of the primary capsule in the capsule space. Furthermore, in the multi-layer network structure, we transmit the information of the classified capsule with the largest activation value in the previous capsule layer to the primary capsules of the next layer, which improves the relevance of the overall structure. We conduct experiments on Fashion-MNIST, SVHN, and CIFAR-10/100, and the experimental results show that our method can improve the performance of the CapsNet more effectively than other state-of-the-arts.

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“…An optimization of the routing strategy and a new routing approach proposed in reference [14] outperformed the dynamic routing method in reference [4]. The Multi-Lane Capsule Network [15] divide the original Capsule Network [4] into multiple lanes to learn different dimensions of vectors that represent distinct features. The Diverse Capsule Network [8] uses three-level capsule layers to learn diverse features and concentrates the features into multi-dimensional vectors.…”

Section: Related Workmentioning

confidence: 99%

DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing

Jia

Huang

2020

Applied Sciences

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Capsule Network (CapsNet) is a methodology with good prospects in visual tasks, since it can keep a stronger relationship of spatial information than Convolutional Neural Networks (CNNs). However, the current Capsule Network do not provide performance as expected on several benchmark data sets with complex data and backgrounds. Inspired by the multiple capsules of Diverse Capsule Network (DCNet++) and the Spatial Group-wise Enhance (SGE) mechanism, we propose the Diverse Enhanced Capsule Network (DE-CapsNet), a hierarchical architecture which uses residual convolutional layers and the position-wise dot product to build diverse enhanced primary capsules with various scales of images for complex data. The architecture adopts the Sigmoid function in a dynamic routing algorithm to get a more uniform distribution of routing coefficients which obviously distinguishes the assignment probabilities between capsules. DE-CapsNet achieved state-of-the-art accuracy on Canadian Institute For Advanced Research (CIFAR-10) in the Capsule Network field and provided better performance than the ensemble of seven CapsNets on Fashion-Modified National Institue of Standards and Technology database (F-MNIST) while achieving a 50.3% reduction in the number of parameters.

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The Multi-Lane Capsule Network

Cited by 48 publications

References 7 publications

Deep Tensor Capsule Network

Deep Tensor Capsule Network

An Improved Capsule Network Based on Capsule Filter Routing

DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing

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