11 citations · 23 across the 7 of their papers we have counts for
7 papers
Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning
Yuhang Zhou, Jiangchao Yao, Feng Hong +2
Class incremental learning (CIL) aims to incrementally update a trained model with the new classes of samples (plasticity) while retaining previously learned ability (stability). T…
Swin MAE: Masked Autoencoders for Small Datasets
Zi'an Xu, Yin Dai, Fayu Liu +5
The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require…
A General Traffic Shaping Protocol in E-Commerce
Chenlin Shen, Guangda Huzhang, Yuhang Zhou +2
To approach different business objectives, online traffic shaping algorithms aim at improving exposures of a target set of items, such as boosting the growth of new commodities. Ge…
MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets
Shixiang Feng, Yuhang Zhou, Xiaoman Zhang +2
Annotating multiple organs in 3D medical images is time-consuming and costly. Meanwhile, there exist many single-organ datasets with one specific organ annotated. This paper invest…
On the Robustness of Domain Adaption to Adversarial Attacks
Liyuan Zhang, Yuhang Zhou, Lei Zhang
State-of-the-art deep neural networks (DNNs) have been proved to have excellent performance on unsupervised domain adaption (UDA). However, recent work shows that DNNs perform poor…
Uncertainty-aware Incremental Learning for Multi-organ Segmentation
Yuhang Zhou, Xiaoman Zhang, Shixiang Feng +2
Most existing approaches to train a unified multi-organ segmentation model from several single-organ datasets require simultaneously access multiple datasets during training. In th…
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation
Xiaoman Zhang, Shixiang Feng, Yuhang Zhou +2
Automatic and accurate tumor segmentation on medical images is in high demand to assist physicians with diagnosis and treatment. However, it is difficult to obtain massive amounts…