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Some Improvements on Deep Convolutional Neural Network Based Image Classification [article]

Andrew G. Howard
2013 arXiv   pre-print
We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline.  ...  Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner.  ...  In trod u cti on Deep convolutional neural networks have recently been substantially improving upon the state of the art in image classification and other recognition tasks [2, 6, 10] .  ... 
arXiv:1312.5402v1 fatcat:rkq27xddevbazikkosgjwjeevm

Image Classification and Object Detection Algorithm Based on Convolutional Neural Network

Juan K. Leonard, Group of Network Computation, Division of Mathematics and Computation, The BASE, Chapel Hill, NC 27510, USA
2019 Science Insights  
advantages and disadvantages, time / space used in image classification based on convolutional neural networks are given.  ...  classification In terms of accuracy, the image classification method based on convolutional neural networks is superior to traditional image classification methods.  ...  Taking research in the field of image classification as an example, after AlexNet greatly improved the accuracy of ImagNet's image classification to 84.7%; continuously improved convolutional neural network  ... 
doi:10.15354/si.19.re117 fatcat:r7xmvkx6qjfsdkyep4urmggz24

Deep learning review and discussion of its future development

Zhiying Hao, W. Anggono
2019 MATEC Web of Conferences  
In the first part, the concept of deep learning and the advantages and disadvantages of deep learning are introduced. The second part demonstrates several algorithms for deep learning.  ...  The third part introduces the application areas of deep learning. Then combines the above algorithms and applications to explore the subsequent development of deep learning.  ...  In the convolutional neural network, the pooling layer is used for feature filtering after image convolution to improve the operability of the classification.  ... 
doi:10.1051/matecconf/201927702035 fatcat:tjjjvhc6prgsfmxaku6mn73ltm

Using Convolution Neural Network for Defective Image Classification of Industrial Components

Hao Wu, Zhi Zhou, Fazlullah Khan
2021 Mobile Information Systems  
For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task.  ...  The paper proposes a deep learning-based artificially intelligent system that can quickly train and identify faulty images.  ...  Ben [16] proposed a group optimization block structure to evolve the CNN model deeply and established a depth network for image classification based on a convolutional neural network.  ... 
doi:10.1155/2021/9092589 fatcat:sep3lpggzfbglfbjuofu3cysry

Improved Image Classification Algorithm Based on Convolutional Neural Network

Xin Li, Luyu Dong, Mengting Li
2021 OALib  
This article mainly introduces the image classification algorithm research based on the improved convolution neural network and some improvement ideas for the research of the classification based on the  ...  convolution neural network.  ...  With the rapid development of deep convolutional neural networks, the accuracy of image classification and detection speed have been greatly improved.  ... 
doi:10.4236/oalib.1108228 fatcat:ar2ej4e3nngktdzzgir2fchsou

Image classification and comparision of different Convolutional neural network srtuctures based On Keras

Pooja.V.Magdum, Mahadev.S. Patil
2020 Zenodo  
In this paper, discussed about a deep learning convolutional network structures based on keras.  ...  Deep learning is a technology inspired by the functioning of human brain. Convolutional neural networks (CNN) become very popular for image classification in deep learning.  ...  In this paper, a deep learning convolutional neural network based on keras is deployed using python for binary image classification.  ... 
doi:10.5281/zenodo.3597134 fatcat:b5ryvznxdffr3euhapwq2icnbq

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Qiufang Ma, Liang Cheng
2022 Computational and Mathematical Methods in Medicine  
Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification.  ...  In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the  ...  The innovative contributions of this paper include the following: (1) For image classification and diagnosis, a convolution neural network algorithm based on deep learning is proposed to diagnose and classify  ... 
doi:10.1155/2022/5373624 pmid:35345522 pmcid:PMC8957435 fatcat:xm4xwstbdnfpjg6n7trzr5ptru

STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK

J. Zhang, J. Zhang, Z. Zhao
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test  ...  Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong  ...  SUMMARY After making some research on the existing methods for the classification of polarimetric SAR imagery, we use the deep learning image classification method of convolutional neural network to study  ... 
doi:10.5194/isprs-archives-xlii-3-2263-2018 fatcat:72my3hv7bven7igsbkq4shvnkq

Research on Image Classification Method Based on DCNN

Chao Ma, Shuo Xu, Xianyong Yi, Linyi Li, Chenglong Yu
2020 2020 International Conference on Computer Engineering and Application (ICCEA)  
In view of the above content, this paper proposes an image detection method based on convolutional neural network.  ...  Different from the traditional image classification methods, the deep convolution neural network model can be used for feature learning and image classification at the same time.  ...  This paper proposes a construction strategy based on convolutional neural networks.  ... 
doi:10.1109/iccea50009.2020.00192 fatcat:vy4tmsmilrfunbptm6wtbtgrxe

Natural Image Classification based on Multi-Parameter Optimization in Deep Convolutional Neural Network

Wang Lei, Zhang Yanning, Xi Runping, Ling Lu
2019 International Journal of Performability Engineering  
A deep convolutional neural network based on multi-parameter optimization by the TensorFlow deep learning framework is designed in this paper.  ...  The experiment involves training and testing on the standard natural image data sets in cifar-10 and cifar-100.  ...  Improved Deep Convolutional Neural Network Model In this paper, we propose a deep convolutional neural structure, and improvements are based on Alex Krizhevsky's ImageNet-2010.  ... 
doi:10.23940/ijpe.19.09.p25.25152521 fatcat:2swgsf5ngnfs3fhkarhlonqdhu

Research Progress of Convolutional Neural Network and its Application in Object Detection [article]

Wei Zhang, Zuoxiang Zeng
2020 arXiv   pre-print
With the improvement of computer performance and the increase of data volume, the object detection based on convolutional neural network (CNN) has become the main algorithm for object detection.  ...  This paper summarizes the research progress of convolutional neural networks and their applications in object detection, and focuses on analyzing and discussing a specific idea and method of applying convolutional  ...  Therefore, the object detection based on convolutional neural network contains only three steps: window sliding, image classification and post-processing.  ... 
arXiv:2007.13284v1 fatcat:wfv2zs6ozrd2vcsgoyq5sc42a4

Underwater acoustic signal analysis: preprocessing and classification by deep learning

Hao Wu, Qingzeng Song, Guanghao Jin
2020 Neural Network World  
Then, among these methods, we modified a neural network(LeNet) to fit the dataset that is transformed by the spectrum to improve the classification accuracy.  ...  Recently, deep learning technology has been utilized to achieve good performance in the underwater acoustic signal case. On the other side, there are still some problems should be solved.  ...  Based on the LeNet, we tried to improve the accuracy with some modification.  ... 
doi:10.14311/nnw.2020.30.007 fatcat:fcrooj2nana5rgqhtfzyxynga4

Application of Convolution Network Model Based on Deep Learning in Sports Image Information Detection

Xiaoqiao Zhang, L. Zhang, S. Defilla, W. Chu
2021 E3S Web of Conferences  
Aiming at the problems and shortcomings of the existing sports image information detection based on convolution neural network, this paper proposes the application of convolution network model based on  ...  In recent years, convolution neural network has achieved great success in single image super-resolution detection.  ...  This paper only studies and analyzes the reconstruction model based on convolutional neural network.  ... 
doi:10.1051/e3sconf/202123302024 fatcat:zuv7pa2j4ve7bi3qg35rxvtdfm

Comparison of Different Convolutional Neural Network Structures Based on Keras

Pooja.V.Magdum, Mahadev.S.Patil
2020 Zenodo  
Deep learning is a technology inspired by functioning of human brain. Convolutional neural network (CNN) becomes very popular for image classification in deep learning.  ...  In this paper, it is discussed about the deep learning's convolutional structures based on Keras.  ...  In this paper deep learning convolutional neural network based on keras is deployed using python for binary image classification.  ... 
doi:10.5281/zenodo.3612800 fatcat:qy4ys2b7arcrbjr3losm5u5dly

A Modified Deep Convolutional Neural Network for Brian Abnormalities Detection

Aneesh S Perumprath, Arun R, Vishnu Raj, MUSALIARCOLLEGE OF ENGINEERING AND TEHNOLOGY PATAHANAMTHITTA
2020 International Journal of Engineering Research and  
In this research, Deep Convolutional Neural Networks (DCNN) is one of the widely used deep learning networks for any practical applications.  ...  The application of Modified DCNN (MDCNN) is explored in the context of Magnetic Resonance (MR) brain tumor image classification.  ...  An improved deep learning approach based on human visual perception is proposed for image classification in [6] .  ... 
doi:10.17577/ijertv9is010105 fatcat:pirxc5qdrrfzfin4xko3ouwchy
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