56 citations · 66 across the 2 of their papers we have counts for
2 papers
cs.LG2018★ 10 cited
Fictitious GAN: Training GANs with Historical Models
Hao Ge, Yin Xia, Xu Chen +2
Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as…
cs.CV2014★ 56 cited
Cross-view Action Modeling, Learning and Recognition
Jiang wang, Xiaohan Nie, Yin Xia +2
Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel mult…