8 citations · 19 across the 7 of their papers we have counts for
7 papers
PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
Zhen Huang, Han Li, Shitong Shao +5
The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnos…
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
Shitong Shao, Xu Dai, Lujun Li +3
Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves perf…
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
Shuai Wang, Daoan Zhang, Zipei Yan +2
Source-free domain adaptation aims to adapt deep neural networks using only pre-trained source models and target data. However, accessing the source model still has a potential con…
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models
Shitong Shao, Xiaohan Yuan, Zhen Huang +3
Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expans…
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network
Wei Li, Weiyan Liu, Shitong Shao +1
Deducing the contribution of each agent and assigning the corresponding reward to them is a crucial problem in cooperative Multi-Agent Reinforcement Learning (MARL). Previous studi…
Teaching What You Should Teach: A Data-Based Distillation Method
Shitong Shao, Huanran Chen, Zhen Huang +3
In real teaching scenarios, an excellent teacher always teaches what he (or she) is good at but the student is not. This gives the student the best assistance in making up for his…
Bootstrap Generalization Ability from Loss Landscape Perspective
Huanran Chen, Shitong Shao, Ziyi Wang +4
Domain generalization aims to learn a model that can generalize well on the unseen test dataset, i.e., out-of-distribution data, which has different distribution from the training…