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An EMG-Based Personal Identification Method Using Continuous Wavelet Transform and Convolutional Neural Networks 会议论文
, Nara, Japan, 2019-10
作者:  Lu, Lijing;  Mao, Jingna;  Wang, Wuqi;  Ding, Guangxin;  Zhang, Zhiwei
Adobe PDF(1246Kb)  |  收藏  |  浏览/下载：229/52  |  提交时间：2022/06/14
GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation 会议论文
IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, JUN 16-20, 2019
作者:  Ma, Xinhong;  Zhang, Tianzhu;  Xu, Changsheng
Adobe PDF(7239Kb)  |  收藏  |  浏览/下载：231/49  |  提交时间：2022/06/14
Modeling Transferable Topics for Cross-Target Stance Detection 会议论文
, Paris, France, 2019-7
作者:  Penghui Wei;  Wenji Mao
Adobe PDF(1556Kb)  |  收藏  |  浏览/下载：176/50  |  提交时间：2021/06/16
Analyzing Topics of JUUL Discussions on Social Media Using a Semantics-assisted NMF model 会议论文
, Shenzhen, China, 2019-7-1
作者:  Hejing Liu;  Qiudan Li;  Riheng Yao;  Daniel Dajun Zeng
Adobe PDF(248Kb)  |  收藏  |  浏览/下载：199/81  |  提交时间：2021/05/27
Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation 会议论文
, USA, 2019
作者:  Jian Liang;  Ran He;  Zhenan Sun;  Tieniu Tan
浏览  |  Adobe PDF(607Kb)  |  收藏  |  浏览/下载：314/87  |  提交时间：2019/06/10
Domain Adaptation  
Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation 期刊论文
IEEE Trans. Pattern Anal. Machine Intell., 2019, 卷号: 41, 期号: 5, 页码: 1027-1042
作者:  Jian Liang;  Ran He;  Zhenan Sun;  Tieniu Tan
浏览  |  Adobe PDF(865Kb)  |  收藏  |  浏览/下载：454/151  |  提交时间：2019/06/10
Unsupervised Domain Adaptation  Domain-invariant Projection  Class-clustering  Sampling-and-fusion  
A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 65-83
作者:  Wu, Yuan;  Zhu, Haibing;  Du, Qingxiu;  Tang, Shuming
浏览  |  Adobe PDF(989Kb)  |  收藏  |  浏览/下载：514/218  |  提交时间：2019/05/04
Inertial Measurement Unit (Imu)  Pedestrian Dead-reckoning  Indoor Navigation  Technical Route  General Framework