no code implementations • ECCV 2020 • Ying-Jun Du, Jun Xu, Huan Xiong, Qiang Qiu, Xian-Tong Zhen, Cees G. M. Snoek, Ling Shao
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift.
1 code implementation • ICML 2020 • Xiantong Zhen, Haoliang Sun, Ying-Jun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.
1 code implementation • 23 Apr 2020 • Ying-Jun Du, Jun Xu, Xian-Tong Zhen, Ming-Ming Cheng, Ling Shao
In this paper, we propose a Conditional Variational Image Deraining (CVID) network for better deraining performance, leveraging the exclusive generative ability of Conditional Variational Auto-Encoder (CVAE) on providing diverse predictions for the rainy image.