9 citations · 11 across the 18 of their papers we have counts for
4 papers · 1 filter
Self-Contrastive Graph Diffusion Network
Yixian Ma, Kun Zhan
Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling…
Entropy Neural Estimation for Graph Contrastive Learning
Yixuan Ma, Xiaolin Zhang, Peng Zhang +1
Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. In this paper, we theoretically illustrate that the entropy of a dataset can…
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure Network
Zhibo Tain, Xiaolin Zhang, Peng Zhang +1
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples. However, the effect…
Curriculum Knowledge Switching for Pancreas Segmentation
Yumou Tang, Kun Zhan, Zhibo Tian +3
Pancreas segmentation is challenging due to the small proportion and highly changeable anatomical structure. It motivates us to propose a novel segmentation framework, namely Curri…