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Grassmannian Graph-attentional Landmark Selection for Domain Adaptation
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.
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition
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.
Matrix Variate RBM and Its Applications
In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly model matrix data.
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