25,196 research outputs found
Residual Capsule Network
Indiana University-Purdue University Indianapolis (IUPUI)The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Putting the best of Capsule Network and Residual Network together, we present Residual Capsule Network and 3-Level Residual Capsule Network, a framework that uses the best of Residual Networks and Capsule Networks. The conventional Convolutional layer in Capsule Network is replaced by skip connections like the Residual Networks to decrease the complexity of the Baseline Capsule Network and seven ensemble Capsule Network. We trained our models on MNIST and CIFAR-10 datasets and have seen a significant decrease in the number of parameters when compared to the Baseline models
Residual Capsule Network
Convolution Neural Network (CNN) has been the most influential innovations in the filed of Computer Vision. CNN have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks - CNN need a large dataset, hyperparameter tuning is nontrivial and importantly, they lose all the internal information about pose and transformation to pooling. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. On the other hand, deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Simply adding layers to make the network deep has led to vanishing gradient problem. Residual Networks introduce skip connections to ease the training and have shown evidence that they can give good accuracy with considerable depth. Putting the best of Capsule Network and Residual Network together, we present Residual Capsule Network, a framework that uses the best features of both Residual and Capsule Networks. In the proposed model, the conventional Convolutional layer in Capsule Network is replaced by skip connections like the Residual Networks to decrease the complexity of the Baseline Capsule Network and seven ensemble Capsule Network. We trained our model on MNIST and CIFAR-10 datasets and have noted a significant decrease in the number of parameters when compared to the Baseline models
3-Level Residual Capsule Network for Complex Datasets
The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Residual Capsule Network [15] has put the Residual Network and Capsule Network together. Though it did well on simple dataset such as MNIST, the architecture can be improved to do better on complex datasets like CIFAR-10. This brings us to the idea of 3-Level Residual Capsule which not only decreases the number of parameters when compared to the seven-ensemble model, but also performs better on complex datasets when compared to Residual Capsule Network
RCNX: Residual Capsule Next
Indiana University-Purdue University Indianapolis (IUPUI)Machine learning models are rising every day. Most of the Computer Vision oriented
machine learning models arise from Convolutional Neural Network’s(CNN) basic structure.
Machine learning developers use CNNs extensively in Image classification, Object Recognition,
and Image segmentation. Although CNN produces highly compatible models with
superior accuracy, they have their disadvantages. Estimating pose and transformation for
computer vision applications is a difficult task for CNN. The CNN’s functions are capable of
learning only shift-invariant features of an image. These limitations give machine learning
developers motivation towards generating more complex algorithms.
Search for new machine learning models led to Capsule Networks. This Capsule Network
was able to estimate objects’ pose in an image and recognize transformations to these
objects. Handwritten digit classification is the task for which capsule networks are to solve
at the initial stages. Capsule Networks outperforms all models for the MNIST dataset for
handwritten digits, but to use Capsule networks for image classification is not a straightforward
multiplication of parameters. By replacing the Capsule Network’s initial layer, a
simple Convolutional Layer, with complex architectures in CNNs, authors of Residual Capsule
Network achieved a tremendous change in capsule network applications without a high
number of parameters.
This thesis focuses on improving this recent Residual Capsule Network (RCN) to an
extent where accuracy and model size is optimal for the Image classification task with a
benchmark of the CIFAR-10 dataset. Our search for an exemplary capsule network led to
the invention of RCN2: Residual Capsule Network 2 and RCNX: Residual Capsule NeXt.
RCNX, as the next generation of RCN. They outperform existing architectures in the domain
of Capsule networks, focusing on image classification such as 3-level RCN, DCNet, DC
Net++, Capsule Network, and even outperforms compact CNNs like MobileNet V3.
RCN2 achieved an accuracy of 85.12% with 1.95 Million parameters, and RCNX achieved
89.31% accuracy with 1.58 Million parameters on the CIFAR-10 benchmark
RCN2: Residual Capsule Network V2
Unlike Convolutional Neural Network (CNN), which works on the shift-invariance in image processing, Capsule Networks can understand hierarchical model relations in depth[1]. This aspect of Capsule Networks let them stand out even when models are enormous in size and have accuracy comparable to the CNNs, which are one-tenth of its size. The capsules in various capsule-based networks were cumbersome due to their intricate algorithm. Recent developments in the field of Capsule Networks have contributed to mitigating this problem. This paper focuses on bringing one of the Capsule Network, Residual Capsule Network (RCN) to a comparable size to modern CNNs and thus restating the importance of Capsule Networks. In this paper, Residual Capsule Network V2 (RCN2) is proposed as an efficient and finer version of RCN with a size of 1.95 M parameters and an accuracy of 85.12% for the CIFAR-10 dataset
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table
Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification
Image classification is a challenging problem which aims to identify the
category of object in the image. In recent years, deep Convolutional Neural
Networks (CNNs) have been applied to handle this task, and impressive
improvement has been achieved. However, some research showed the output of CNNs
can be easily altered by adding relatively small perturbations to the input
image, such as modifying few pixels. Recently, Capsule Networks (CapsNets) are
proposed, which can help eliminating this limitation. Experiments on MNIST
dataset revealed that capsules can better characterize the features of object
than CNNs. But it's hard to find a suitable quantitative method to compare the
generalization ability of CNNs and CapsNets. In this paper, we propose a new
image classification task called Top-2 classification to evaluate the
generalization ability of CNNs and CapsNets. The models are trained on single
label image samples same as the traditional image classification task. But in
the test stage, we randomly concatenate two test image samples which contain
different labels, and then use the trained models to predict the top-2 labels
on the unseen newly-created two label image samples. This task can provide us
precise quantitative results to compare the generalization ability of CNNs and
CapsNets. Back to the CapsNet, because it uses Full Connectivity (FC) mechanism
among all capsules, it requires many parameters. To reduce the number of
parameters, we introduce the Parameter-Sharing (PS) mechanism between capsules.
Experiments on five widely used benchmark image datasets demonstrate the method
significantly reduces the number of parameters, without losing the
effectiveness of extracting features. Further, on the Top-2 classification
task, the proposed PS CapsNets obtain impressive higher accuracy compared to
the traditional CNNs and FC CapsNets by a large margin.Comment: This paper is under consideration at Computer Vision and Image
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