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semisupervised learning-based depth estimation with semantic inference guidance

Science China(Technological Sciences) 2022年第5期65卷 1098-1106页

作者： ZHANG Yan FAN XiaoPeng ZHAO DeBin Department of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given *** it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated *** pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or *** widespread application of deep learning further intensifies the demand for large amounts of annotated ***,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of *** this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given ***,semantic information is employed to make the depth inference more ***,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic *** compare our model with state-of-the-art *** experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.

关键词： depth estimation semisupervised learning semantic information neural networks

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A novel approach for unlabeled samples in radiation source identification

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Journal of Systems Engineering and Electronics 2022年第2期33卷 354-359页

作者： YANG Haifen ZHANG Hao WANG Houjun GUO Zhengyang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source ***, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.

关键词： radiation source identification deep learning semisupervised learning

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Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1258-1273页

作者： Degang Wang Lianru Gao Ying Qu Xu Sun Wenzhi Liao Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Faculty of Geographical Science Beijing Normal UniversityBeijingChina Flanders Make LommelBelgium Ghent University GhentBelgium

Most unsupervised or semisupervised hyperspectral anomaly detection(HAD)methods train background reconstruction models in the original spectral ***,due to the noise and spatial resolution limitations,there may be a lack of discrimination between backgrounds and *** makes it easy for the autoencoder to capture the lowlevel features shared between the two,thereby increasing the difficulty of separating anomalies from the backgrounds,which runs counter to the purpose of *** this end,the authors map the original spectrums to the fractional Fourier domain(FrFD)and reformulate it as a mapping task in which restoration errors are employed to distinguish background and *** study proposes a novel frequency‐to‐spectrum mapping generative adversarial network for ***,the depth separable features of backgrounds and anomalies are enhanced in the *** to the semisupervised approach,FTSGAN needs to learn the embedded features of the backgrounds,thus mapping and restoring them from the FrFD to the original spectral *** strategy effectively prevents the model from focussing on the numerical equivalence of input and output,and restricts the ability of FTSGAN to restore *** comparison and analysis of the experiments verify that the proposed method is competitive.

关键词： deep learning generative adversarial network hyperspectral image neural network semisupervised learning

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semisupervised Sparse Multilinear Discriminant Analysis

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Journal of Computer Science & Technology 2014年第6期29卷 1058-1071页

作者：黄锴张丽清 MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and EngineeringShanghai Jiao Tong University IEEE

Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.

关键词： ECG analysis semisupervised learning sparse coding dimension reduction tensor learning approach

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semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems Science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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