no code implementations • 7 Sep 2021 • Bin Sun, Shaofan Wang, Dehui Kong, Jinghua Li, BaoCai Yin
GGLS presents a landmark selection scheme using attention-induced neighbors of the graphical structure of samples and performs distribution adaptation and knowledge adaptation over Grassmann manifold.
no code implementations • 25 May 2021 • Bin Sun, Dehui Kong, Shaofan Wang, Jinghua Li, BaoCai Yin, Xiaonan Luo
In the sampling stage, we utilize a generative adversarial networks (GAN) trained by action features and word vectors of seen classes to synthesize the action features of unseen classes, which can balance the training sample data of seen classes and unseen classes.
no code implementations • 5 Jan 2016 • Guanglei Qi, Yanfeng Sun, Junbin Gao, Yongli Hu, Jinghua Li
In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly model matrix data.