Wentao Rong

TL;DR: The calcification was characterized by descriptors obtained from deep learning and handcrafted descriptors, which achieved a classification precision of 89.32% and sensitivity of 86.89% using the filtered deep features, which is the best performance among all the feature sets.

TL;DR: The proposed method not only preserves the structure of the most informative individual view, but also discovers a latent common structure across all views as well as outperforms several state-of-the-art multi-view subspace clustering methods.

TL;DR: In this paper , a generative adversarial network (GAN)-based cloud removal framework using a distortion coding network combined with compound loss functions (DC-GAN-CL) is proposed to accurately reproduce surface information that has been contaminated by clouds.

TL;DR: In this paper, the authors proposed a deep wavelet network to solve the problem of training a large number of parameters in deep neural networks, which is notoriously known that its training needs a considerable time cost to refine a large amount of parameters.

TL;DR: In this paper, a multi-metric similarity graph refinement and fusion method for multi-view clustering is proposed, which constructs multiple similarity graphs for each view by different metric, exploit a novel refined similarity through symmetric conditional probability to preserve the important similarity information and finally adaptively fuse multiple refined similarity graphs to an informative unified one.