scholarly journals Self-paced Convolutional Neural Networks

2017 ◽  
Author(s):  
Hao Li ◽  
Maoguo Gong
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Stationary Solution ◽  
Convolutional Neural Networks ◽  
Reliable Data ◽  
Experimental Results ◽  
Theoretical Studies ◽  
Convolutional Network ◽  
Sample Weights ◽  
Learning Rates

Convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks. In order to distinguish the reliable data from the noisy and confusing data, we improve CNNs with self-paced learning (SPL) for enhancing the learning robustness of CNNs. In the proposed self-paced convolutional network (SPCN), each sample is assigned to a weight to reflect the easiness of the sample. Then a dynamic self-paced function is incorporated into the leaning objective of CNN to jointly learn the parameters of CNN and the latent weight variable. SPCN learns the samples from easy to complex and the sample weights can dynamically control the learning rates for converging to better values. To gain more insights of SPCN, theoretical studies are conducted to show that SPCN converges to a stationary solution and is robust to the noisy and confusing data. Experimental results on MNIST and rectangles datasets demonstrate that the proposed method outperforms baseline methods.

scholarly journals Recognizing Ancient Characters from Tamil Palm Leaf Manuscripts using Convolution Based Deep Learning

2019 ◽  
Vol 8 (3) ◽  
pp. 6873-6880
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Convolutional Network ◽  
Palm Leaf

Palm leaf manuscripts has been one of the ancient writing methods but the palm leaf manuscripts content requires to be inscribed in a new set of leaves. This study has provided a solution to save the contents in palm leaf manuscripts by recognizing the handwritten Tamil characters in manuscripts and storing them digitally. Character recognition is one of the most essential fields of pattern recognition and image processing. Generally Optical character recognition is the method of e-translation of typewritten text or handwritten images into machine editable text. The handwritten Tamil character recognition has been one of the challenging and active areas of research in the field of pattern recognition and image processing. In this study a trial was made to identify Tamil handwritten characters without extraction of feature using convolutional neural networks. This study uses convolutional neural networks for recognizing and classifying the Tamil palm leaf manuscripts of characters from separated character images. The convolutional neural network is a deep learning approach for which it does not need to retrieve features and also a rapid approach for character recognition. In the proposed system every character is expanded to needed pixels. The expanded characters have predetermined pixels and these pixels are considered as characteristics for neural network training. The trained network is employed for recognition and classification. Convolutional Network Model development contains convolution layer, Relu layer, pooling layer, fully connected layer. The ancient Tamil character dataset of 60 varying class has been created. The outputs reveal that the proposed approach generates better rates of recognition than that of schemes based on feature extraction for handwritten character recognition. The accuracy of the proposed approach has been identified as 97% which shows that the proposed approach is effective in terms of recognition of ancient characters.

TRANSITION TO CONVOLUTIONAL NEURAL NETWORKS IN RADAR PROBLEMS

2019 ◽  
pp. 96-99
Author(s):  
K. Maystrenko ◽  
A. Budilov ◽  
D. Afanasev
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Target Detection ◽  
Convolutional Neural Networks ◽  
Network Topology ◽  
Subject Areas ◽  
The Subject ◽  
Printed Materials ◽  
Hardware Costs

Goal. Identify trends and prospects for the development of radar in terms of the use of convolutional neural networks for target detection. Materials and methods. Analysis of relevant printed materials related to the subject areas of radar and convolutional neural networks. Results. The transition to convolutional neural networks in the field of radar is considered. A review of papers on the use of convolutional neural networks in pattern recognition problems, in particular, in the radar problem, is carried out. Hardware costs for the implementation of convolutional neural networks are analyzed. Conclusion. The conclusion is made about the need to create a methodology for selecting a network topology depending on the parameters of the radar task.

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

scholarly journals Exploiting Weak Ties in Incomplete Network Datasets Using Simplified Graph Convolutional Neural Networks

2020 ◽  
Vol 2 (2) ◽  
pp. 125-146 ◽  
Author(s):  
Neda H. Bidoki ◽  
Alexander V. Mantzaris ◽  
Gita Sukthankar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Centrality Measures ◽  
Weak Ties ◽  
Original Graph ◽  
Convolutional Network ◽  
Strong Ties ◽  
Network Nodes ◽  
Node Removal ◽  
Selection Of

This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. This is done by applying the methodological framework of the Simplified Graph Convolutional Neural Network (SGC) to two academic publication datasets: Cora and Citeseer. The performance of SGC is compared to the original Graph Convolutional Network (GCN) framework. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of SGC. When removal is based on a more localized selection of nodes, augmenting the network with both strong-ties and weak-ties provides a benefit, indicating that SGC successfully leverages local information of network nodes.

scholarly journals Checkerboard artifacts free convolutional neural networks

2019 ◽  
Vol 8 ◽  
Author(s):  
Yusuke Sugawara ◽  
Sayaka Shiota ◽  
Hitoshi Kiya
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Experimental Results ◽  
The Novel ◽  
Multirate Systems ◽  
Conventional Condition ◽  
Novel Structure ◽  
Avoidance Condition

AbstractIt is well-known that a number of convolutional neural networks (CNNs) generate checkerboard artifacts in both of two processes: forward-propagation of upsampling layers and backpropagation of convolutional layers. A condition for avoiding the artifacts is proposed in this paper. So far, these artifacts have been studied mainly for linear multirate systems, but the conventional condition for avoiding them cannot be applied to CNNs due to the non-linearity of CNNs. We extend the avoidance condition for CNNs and apply the proposed structure to typical CNNs to confirm whether the novel structure is effective. Experimental results demonstrate that the proposed structure can perfectly avoid generating checkerboard artifacts while keeping the excellent properties that CNNs have.

scholarly journals An Efficient Algorithm for Cardiac Arrhythmia Classification Using Ensemble of Depthwise Separable Convolutional Neural Networks

2020 ◽  
Vol 10 (2) ◽  
pp. 483 ◽  
Author(s):  
Eko Ihsanto ◽  
Kalamullah Ramli ◽  
Dodi Sudiana ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Cardiac Arrhythmia ◽  
Convolutional Neural Networks ◽  
Computational Cost ◽  
Training Data ◽  
Qrs Detection ◽  
Convolutional Network ◽  
Novel Method ◽  
Electrocardiogram Ecg

Many algorithms have been developed for automated electrocardiogram (ECG) classification. Due to the non-stationary nature of the ECG signal, it is rather challenging to use traditional handcraft methods, such as time-based analysis of feature extraction and classification, to pave the way for machine learning implementation. This paper proposed a novel method, i.e., the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats. Using our proposed method, the four stages of ECG classification, i.e., QRS detection, preprocessing, feature extraction, and classification, were reduced to two steps only, i.e., QRS detection and classification. No preprocessing method was required while feature extraction was combined with classification. Moreover, to reduce the computational cost while maintaining its accuracy, several techniques were implemented, including All Convolutional Network (ACN), Batch Normalization (BN), and ensemble convolutional neural networks. The performance of the proposed ensemble CNNs were evaluated using the MIT-BIH arrythmia database. In the training phase, around 22% of the 110,057 beats data extracted from 48 records were utilized. Using only these 22% labeled training data, our proposed algorithm was able to classify the remaining 78% of the database into 16 classes. Furthermore, the sensitivity ( S n ), specificity ( S p ), and positive predictivity ( P p ), and accuracy ( A c c ) are 99.03%, 99.94%, 99.03%, and 99.88%, respectively. The proposed algorithm required around 180 μs, which is suitable for real time application. These results showed that our proposed method outperformed other state of the art methods.

scholarly journals Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method

2018 ◽  
Author(s):  
Shuqin Gu ◽  
Yuexian Hou ◽  
Lipeng Zhang ◽  
Yazhou Zhang
Keyword(s):  
Neural Networks ◽  
Ensemble Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Experimental Results ◽  
Excellent Performance ◽  
Hidden Layer ◽  
Base Learner ◽  
Fully Connected

Although Deep Neural Networks (DNNs) have achieved excellent performance in many tasks, improving the generalization capacity of DNNs still remains a challenge. In this work, we propose a novel regularizer named Ensemble-based Decorrelation Method (EDM), which is motivated by the idea of the ensemble learning to improve generalization capacity of DNNs. EDM can be applied to hidden layers in fully connected neural networks or convolutional neural networks. We treat each hidden layer as an ensemble of several base learners through dividing all the hidden units into several non-overlap groups, and each group will be viewed as a base learner. EDM encourages DNNs to learn more diverse representations by minimizing the covariance between all base learners during the training step. Experimental results on MNIST and CIFAR datasets demonstrate that EDM can effectively reduce the overfitting and improve the generalization capacity of DNNs  

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