Query: Convolutions Capsule Network

title: A deep learning method for motor fault diagnosis based on a capsule network with gate structure dilated convolutions
abstract: Motor fault diagnosis is critical to predictive maintenance of electrical motor condition monitoring, whereas the conventional motor fault diagnosis method cannot effectively diagnose conditions caused by motors' complex structure, non stationary signals and mechanical big data. To solve the mentioned problems and enhance the fault diagnostic accuracy and generalization performance under different actual motor conditions, this study proposes an efficient, noise resistant, end to end deep learning algorithm based on a novel capsule network with gate structure dilated convolutions (GDCCN) for motor fault diagnosis; such algorithm is subtly incorporated with the input gate structure of long short term memory network (LSTM) the dilated convolutions, as well as the capsule network. In the GDCCN model, the raw vibration signals are directly fed into the input gate structure of LSTM, which are employed to effectively remove noise and harvest more valuable information from the input sample. The dilated convolution is exploited in the output denoising feature maps to exponentially expand the receptive field of convolution kernel, so more redundant information can be acquired to reduce the effect of randomness. The capsule network is introduced to generate a set of vector neurons to represent an entity existing in the feature maps; as a result, more specific feature representations can be extracted, and the feature can be comprehended, thereby enhancing the diagnostic accuracy. As revealed from the experimental results, the GDCCN based intelligent motor fault diagnosis method outperforms the other classical DL algorithms in diagnosis accuracy, noise resistance, generalization and transfer learning performance of different workloads.

title: A deep learning method for motor fault diagnosis based on a capsule network with gate structure dilated convolutions

title: S CCCapsule: Pneumonia detection in chest X ray images using skip connected convolutions and capsule neural network
abstract: The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip connected convolutional capsule (S CCCapsule) (ii) Integrated skip connected convolutional capsules (ISCC) and (iii) Ensemble skip connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children's Medical Center (GWCMC) Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC) Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.

title: DCG Net: Dynamic Capsule Graph Convolutional Network for Point Clouds
abstract: This article introduces DCG Net (Dynamic Capsule Graph Network) to analyze point clouds for the tasks of classification and segmentation. DCG Net aggregates point cloud features to build and update the graphs based on the dynamic routing mechanism of capsule networks at each layer of a convolutional network. The first layer of DGC Net exploits the geometrical attributes of the point cloud to build a graph by neighborhood aggregation while the deeper layers of the network dynamically update the graph based on the feature space of convolutions. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet Part. Our experimental results demonstrate that DCG Net achieves state of the art performance on public datasets, 93.4% accuracy on ModelNet40, and 85.4% instance mIoU (mean Intersection over Union) on ShapeNet Part.

title: A Branching and Merging Convolutional Network with Homogeneous Filter Capsules
abstract: We present a convolutional neural network design with additional branches after certain convolutions so that we can extract features with differing effective receptive fields and levels of abstraction. From each branch, we transform each of the final filters into a pair of homogeneous vector capsules. As the capsules are formed from entire filters, we refer to them as filter capsules. We then compare three methods for merging the branches merging with equal weight and merging with learned weights, with two different weight initialization strategies. This design, in combination with a domain specific set of randomly applied augmentation techniques, establishes a new state of the art for the MNIST dataset with an accuracy of 99.84% for an ensemble of these models, as well as establishing a new state of the art for a single model (99.79% accurate) These accuracies were achieved with a 75% reduction in both the number of parameters and the number of epochs of training relative to the previously best performing capsule network on MNIST. All training was performed using the Adam optimizer and experienced no overfitting.

title: Dense and Diverse Capsule Networks: Making the Capsules Learn Better
abstract: Past few years have witnessed exponential growth of interest in deep learning methodologies with rapidly improving accuracies and reduced computational complexity. In particular, architectures using Convolutional Neural Networks (CNNs) have produced state of the art performances for image classification and object recognition tasks. Recently, Capsule Networks (CapsNet) achieved significant increase in performance by addressing an inherent limitation of CNNs in encoding pose and deformation. Inspired by such advancement, we asked ourselves, can we do better? We propose Dense Capsule Networks (DCNet) and Diverse Capsule Networks (DCNet+ The two proposed frameworks customize the CapsNet by replacing the standard convolutional layers with densely connected convolutions. This helps in incorporating feature maps learned by different layers in forming the primary capsules. DCNet, essentially adds a deeper convolution network, which leads to learning of discriminative feature maps. Additionally, DCNet+ uses a hierarchical architecture to learn capsules that represent spatial information in a fine to coarser manner, which makes it more efficient for learning complex data. Experiments on image classification task using benchmark datasets demonstrate the efficacy of the proposed architectures. DCNet achieves state of the art performance (99.75% on MNIST dataset with twenty fold decrease in total training iterations, over the conventional CapsNet. Furthermore, DCNet+ performs better than CapsNet on SVHN dataset (96.90% and outperforms the ensemble of seven CapsNet models on CIFAR 10 by 0.31% with seven fold decrease in number of parameters.

title: Multi level Dense Capsule Networks
abstract: Past few years have witnessed an exponential growth of interest in deep learning methodologies with rapidly improving accuracy and reduced computational complexity. In particular, architectures using Convolutional Neural Networks (CNNs) have produced state of the art performances for image classification and object recognition tasks. Recently, Capsule Networks (CapsNets) achieved a significant increase in performance by addressing an inherent limitation of CNNs in encoding pose and deformation. Inspired by such an advancement, we propose Multi level Dense Capsule Networks (multi level DCNets) The proposed framework customizes CapsNet by adding multi level capsules and replacing the standard convolutional layers with densely connected convolutions. A single level DCNet essentially adds a deeper convolution network, which leads to learning of discriminative feature maps learned by different layers to form the primary capsules. Additionally, multi level capsule networks uses a hierarchical architecture to learn new capsules from former capsules that represent spatial information in a fine to coarser manner, which makes it more efficient for learning complex data. Experiments on image classification task using benchmark datasets demonstrate the efficacy of the proposed architectures. DCNet achieves state of the art performance (99.75% on the MNIST dataset with approximately twenty fold decrease in total training iterations, over the conventional CapsNet. Furthermore, multi level DCNet performs better than CapsNet on SVHN dataset (96.90% and outperforms the ensemble of seven CapsNet models on CIFAR 10 by )0.31% with seven fold decrease in the number of parameters. Source codes, models and figures are available at https:/github.com/ssrp/Multi level DCNet.

title: COVID CAPS: A capsule network based framework for identification of COVID 19 cases from X ray images
abstract: Novel Coronavirus disease (COVID 19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID 19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID 19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID 19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human centered diagnosis of COVID 19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN) based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs) to facilitate identification of positive COVID 19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID 19. Our results based on a dataset of X ray images show that COVID CAPS has advantage over previous CNN based models. COVID CAPS achieved an Accuracy of 95.7% Sensitivity of 90% Specificity of 95.8% and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID CAPS, pre training and transfer learning are utilized based on a new dataset constructed from an external dataset of X ray images. This is in contrary to existing works on COVID 19 detection where pre training is performed based on natural images. Pre training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%

title: Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network
abstract: Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state of the art approaches in apoptosis classification use deep convolutional neural networks (CNNs) However, these networks are not efficient in encoding the part whole relationships, thus requiring a large number of training samples to achieve robust generalization. This paper proposes an efficient variant of capsule networks (CapsNets) as an alternative to CNNs. Extensive experimental results demonstrate that the proposed CapsNets achieve competitive performances in target cell apoptosis classification, while significantly outperforming CNNs when the number of training samples is small. To utilize temporal information within microscopy videos, we propose a recurrent CapsNet constructed by stacking a CapsNet and a bi directional long short term recurrent structure. Our experiments show that when considering temporal constraints, the recurrent CapsNet achieves 93.8% accuracy and makes significantly more consistent prediction than NNs.

title: A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis
abstract: Bearing fault diagnosis is a significant part of rotating machine health monitoring. In the era of big data, various data driven methods, which are mainly based on deep learning (DL) have been applied in the field of fault diagnosis. As a common deep learning method, convolutional neural networks play an important role in the field of image classification. To take advantage of existing research results of convolutional neural networks that were originally designed for images, short time Fourier transform is applied to convert signals to two dimensional graphs. However, the traditional convolutional neural network with max pooling layers may disregard the positional relationship between features. Inspired by dynamic routing capsule net, a novel capsule network with an inception block and a regression branch is proposed. First, during the preprocessing stage, the one dimensional signals are transformed into a time frequency graph. Second, the graph data are fed into the network, and two convolution layers are applied to extract higher information; following that, an inception block is applied to the output feature maps to improve the nonlinearity of the capsule. Then, after dynamic routing, the lengths of the capsules are used to classify the fault category. There are two other branches: one uses the longest capsule to regress the damage size of the capsule, and the other reconstructs the input graph. To verify the generalization ability of the proposed model, comparisons among the proposed model, conventional methods and state of the art convolutional neural network models are carried out.