检索结果-南通市图书馆

Transferable Features from 1D-convolutional network for Industrial Malware Classification

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2022年第2期130卷 1003-1016页

作者： LiweiWang Jiankun Sun Xiong Luo Xi Yang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Shunde Graduate School University of Science and Technology BeijingFoshan528399China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149China

With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and *** identify and classify new malware variants,different types of deep learning models have been widely explored ***,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ***,in current practical applications,an ample supply of data is absent in most specific industrial malware detection *** learning as an effective approach can be used to alleviate the influence of the small sample size *** addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware *** this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning *** experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.

关键词： Transfer learning malware classification sequence data modeling convolutional network

Spatial and Contextual Path network for Image Inpainting

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2024年第2期39卷 115-133页

作者： Dengyong Zhang Yuting Zhao Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinTaiwan Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Image inpainting is a kind of use known area of information technology to repair the loss or damage to the *** feature extraction is the core of image *** enough space for information and a larger receptive field is very important to realize high-precision image ***,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same *** order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path *** the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial *** the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept *** order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two *** fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight *** of images in order to extract context information,we add attention to the context path refinement *** modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective *** show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements.

关键词： Image inpainting attention deep learning convolutional network

Student Action Recognition Based on Deep convolutional Generative Adversarial network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Student Action Recognition Based on Deep Convolutional Gener...

第32届中国控制与决策会议

作者： Yanyan Cheng Zhongjian Dai Ye Ji Simin Li Zhiyang Jia Kaoru Hirota Yaping Dai School of Automation Beijing Institute of Technology

Student action analysis plays an import role in learning and teaching. In order to improve the accuracy of student action recognition in classroom learning, a Deep convolutional Generative Adversarial network for Student Action Recognition（DCGANSAR） method is proposed. The method contains two stages: constructing the Deep convolutional Generative Adversarial network（DCGAN） to obtain pre-trained weights in the discriminator, and using the discriminator of DCGAN to classify actions. The advantage is that the confrontation between the generator and the discriminator in DCGAN makes the discriminator get stronger, and effective weights are obtained. The pre-trained weights are beneficial for student action recognition, so the accuracy of recognition is improved. Experiments are conducted on the self-built student action dataset. The experimental results demonstrate that the proposed method recognizes student action with high accuracy and fast convergence speed.

关键词： Student Action Recognition Generative Adversarial network(GAN) convolutional network

Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

在线全文

cnki会议

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第9期46卷 2711-2724页

作者： Vidit Kumar Hemant Petwal Ajay Krishan Gairola Pareshwar Prasad Barmola Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India School of Computer Science Engineering and Technology Bennett UniversityGreater Noida201310Uttar PradeshIndia

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query *** key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered,and dissimilar images are separated in the low embedding *** works primarily focused on defining local structure loss functions like triplet loss,pairwise loss,***,training via these approaches takes a long training time,and they have poor ***,representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen *** paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these *** the proposed work,class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class *** other instances are treated as negatives within the same *** a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded *** proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results(85.38%recall@1 for CARS-196%and 70.13%recall@1 for CUB-200)compared to other existing methods.

关键词： convolutional network zero-shot learning fine-grained image retrieval image representation image retrieval intra-class diversity feature learning

Image Denoising Using an Improved Generative Adversarial network with Wasserstein Distance

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Image Denoising Using an Improved Generative Adversarial Net...

第40届中国控制会议

作者： Qian Wang Han Liu Guo Xie Youmin Zhang Xi'an University of Technology Concordia University

The image denoising discriminant model has received extensive attention in recent years due to its good denoising performance. In order to solve the problems of denoising of traditional generative adversarial networks, which are difficult to train and easy to collapse, traditional denoising methods will damage the visibility of important structural details after the image is denoised, this paper proposes an improved generative adversarial network algorithm. The novel algorithm obtains more image features by adding multi-level convolution of the generative network, and adds multiple residual blocks and global residuals to extract and learn the features of the input noisy image to avoid the loss of features. The network uses the weighted sum of feature loss and perceptual loss, which can effectively retain the detailed information of the image while removing noise. In the experiments, we selected PSNR and SSIM as the indicators, and the experimental results show that the novel algorithm can effectively remove image noise and improve the visual perception of the image.

关键词： Image Denoising Generative Adversarial network convolutional network Residual network

Complex-Valued Densely Connected convolutional networks

在线全文

cnki会议

学校读者我要写书评

暂无评论

国际计算机前沿大会会议论文集 2020年第1期 299-309页

作者： Wenhan Li Wenqing Xie Zhifang Wang Department of Electronic Engineering Heilongjiang UniversityHarbin 150080China

In recent years,deep learning has made significant progress in computer ***,most studies focus on the algorithms in real *** number can have richer characterization capabilities and use fewer training *** paper proposes a complex-valued densely connected convolutional network,which is complex-valued *** generalizes the network structure of real-valued DenseNets to complex field and constructs the basic architectures including complex dense block and complex transition *** were performed on the CIFAR-10 database and CIFAR-100 *** results show the proposed algorithm has a lower error rate and fewer parameters than real-valued DenseNets and complex-valued ResNets.

关键词： Deep learning Complex field convolutional network

Classification of Lunar Lineament Structures Based on GoogLeNet

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Classification of Lunar Lineament Structures Based on GoogLe...

作者： Pengcheng YAN Jiahui LIU Xiaolin TIAN Faculty of Information Technology Macau University of Science and Technology Lunar and Planetary Science Laboratory/Space Science Institute Macau University of Science and Technology

This article aims to give a brief introduction on using GoogLeNet on Lunar lineament structure classification,mainly focusing on non-lineament structure,Lunar ridges and Lunar *** same network which was used to identify the non-lineament and lineament lunar structure has already reached the accuracy of around 84% based on a total sample of *** the so called three-type classification with around 300 unbalanced distribution sample,the result is around 85% to 88%,especially on identifying the lunar ridge,which has already reached over 98%.

关键词： convolutional network GoogLeNet Lunar Lineament Structure

Garbage Classification Detection Based on Improved YOLOV4

在线全文

cnki会议

学校读者我要写书评

暂无评论

Garbage Classification Detection Based on Improved YOLOV4

作者： Qingqiang Chen Qianghua Xiong School of Mechatronics and Control Engineering Shenzhen University School of Electronic Engineering and Intelligentization Dongguan University of Technology School of Computer Dongguan University of Technology

As the rate of garbage generation gradually increases, the past garbage disposal methods will be eliminated, so the classification of garbage has become an inevitable choice. The multi-category classification of garbage and the accuracy of recognition have also become the focus of attention. Aiming at the problems of single category, few types of objects and low accuracy in existing garbage classification algorithms. This paper proposes to use the improved YOLOV4 network framework to detect 3 categories, a total of 15 objects, and find that the average accuracy is 64%, Frame per second 92 f/s. It turns out that the improved YOLOV4 can better detect garbage categories and is suitable for embedded devices.

关键词： YOLOV4 Computer Vision Classification convolutional network