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GENERATIVE: 作品数：537被引量：766H指数：13; 导出分析报告; 相关作者：郝志桃王东李昌华李曙光赵凯华更多>>; 相关机构：华中师范大学浙江省交通规划设计研究院有限公司西安建筑科技大学上海电力大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金中国博士后科学基金国家社会科学基金高等学校学科创新引智计划更多>>

Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution: 《Tsinghua Science and Technology》2024年第6期1796-1809,共14页Ramakrishna Akella Sravan Kumar Gunturi Dipu Sarkar; In the critical field of electrical grid maintenance,ensuring the integrity of power line insulators is a primary concern.This study introduces an innovative approach for monitoring the condition of insulators using a...; 关键词：DCGAN generative adversarial networks insulators SRGAN YOLO

Feature-Grounded Single-Stage Text-to-Image Generation: 《Tsinghua Science and Technology》2024年第2期469-480,共12页Yuan Zhou Peng Wang Lei Xiang Haofeng Zhang; supported by the National Natural Science Foundation of China(No.61872187).; Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)framework.However,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image ...; 关键词：text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)

Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images被引量：1: 《Tsinghua Science and Technology》2024年第1期197-206,共10页Weizhi Du Shihao Tian; Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose,such as in low-field magnetic resonance imaging(MRI).Howev...; 关键词：SUPER-RESOLUTION image reconstruction TRANSFORMER generative adversarial network(GAN)

Curricular Robust Reinforcement Learning via GAN-Based Perturbation Through Continuously Scheduled Task Sequence被引量：1: 《Tsinghua Science and Technology》2023年第1期27-38,共12页Yike Li Yunzhe Tian Endong Tong Wenjia Niu Yingxiao Xiang Tong Chen Yalun Wu Jiqiang Liu; supported by the National Natural Science Foundation of China (Nos.61972025,61802389,61672092,U1811264,and 61966009);the National Key R&D Program of China (Nos.2020YFB1005604 and 2020YFB2103802).; Reinforcement learning(RL),one of three branches of machine learning,aims for autonomous learning and is now greatly driving the artificial intelligence development,especially in autonomous distributed systems,such as...; 关键词：robust reinforcement learning generative adversarial network(GAN)based model curricular learning

Adversarial Training for Supervised Relation Extraction被引量：2: 《Tsinghua Science and Technology》2022年第3期610-618,共9页Yanhua Yu Kanghao He Jie Li; supported in part by the National Natural Science Foundation of China (Nos. U1936104 and 2020JCJQ-ZD-012)。; Most supervised methods for relation extraction(RE) involve time-consuming human annotation. Distant supervision for RE is an efficient method to obtain large corpora that contains thousands of instances and various r...; 关键词：relation extraction piecewise convolution neural network adversarial training generative adversarial network

Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation被引量：2: 《Tsinghua Science and Technology》2022年第1期68-78,共11页Hongxia Deng Yuefang Zhang Ran Li Chunxiang Hu Zijian Feng Haifang Li; supported in part by the National Natural Science Foundation of China(Nos.61873178and 61976150);Natural Science Foundation of Shanxi Province(Nos.201801D21135 and 201901D111091);Key Research and Development Projects of Shanxi Province(No.201803D421047)。; This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus.Different convolutional configurations were proposed to capture information obtained by a s...; 关键词：magnetic resonance imaging generative adversarial network residual network attention mechanism

A Cascade Model-Aware Generative Adversarial Example Detection Method: 《Tsinghua Science and Technology》2021年第6期800-812,共13页Keji Han Yun Li Bin Xia; supported by the National Natural Science Foundation of China (Nos.61603197,61772284,and 61876091)。; Deep Neural Networks(DNNs) are demonstrated to be vulnerable to adversarial examples, which are elaborately crafted to fool learning models. Since the accuracy and robustness of DNNs are at odds for the adversarial tr...; 关键词：information security Deep Neural Network(DNN) adversarial example detection

A Generative Method for Steganography by Cover Synthesis with Auxiliary Semantics被引量：6: 《Tsinghua Science and Technology》2020年第4期516-527,共12页Zhuo Zhang Guangyuan Fu Rongrong Ni Jia Liu Xiaoyuan Yang; supported by the National Natural Science Foundation of China(NSFC)(Nos.61872384 and61672090).; Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capac...; 关键词：information hiding STEGANOGRAPHY STEGANOGRAPHY without modification STEGANOGRAPHY by COVER Synthesis(SCS) GENERATIVE adversarial networks

A Survey of Image Synthesis and Editing with Generative Adversarial Networks被引量：20: 《Tsinghua Science and Technology》2017年第6期660-674,共15页Xian Wu Kun Xu Peter Hall; supported by the National Key Technology R&D Program(No.2016YFB1001402);the National Natural Science Foundation of China(No.61521002);the Joint NSFC-ISF Research Program(No.61561146393);Research Grant of Beijing Higher Institution Engineering Research Center and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology;supported by the EPSRC CDE(No.EP/L016540/1); This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due...; 关键词：image synthesis image editing constrained image synthesis generative adversarial networks imageto-image translation

An Integrated Application of Neural Network ,Fuzzy and Expert Systems for Machining Operation Sequencing: 《Tsinghua Science and Technology》1999年第4期1632-1637,共6页王先逵刘成颖; Apartis described using features.Aneuro fuzzy system then determines the machining sequence for each feature.Previous process plans were utilized to build,test,and validate the Neuro Fuzzy Network (NFN). Parts hav...; 关键词：machining sequence neuro fuzzy expertsystem semi generative group technology

GENERATIVE