Search Results for author: YuHang Zhou

Found 8 papers, 1 papers with code

AutoMine: An Unmanned Mine Dataset

no code implementations CVPR 2022 Yuchen Li, Zixuan Li, Siyu Teng, Yu Zhang, YuHang Zhou, Yuchang Zhu, Dongpu Cao, Bin Tian, Yunfeng Ai, Zhe XuanYuan, Long Chen

The main contributions of the AutoMine dataset are as follows: 1. The first autonomous driving dataset for perception and localization in mine scenarios.

Autonomous Driving

A General Traffic Shaping Protocol in E-Commerce

no code implementations30 Dec 2021 Chenlin Shen, Guangda Huzhang, YuHang Zhou, Chen Liang, Qing Da

Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation.

MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets

no code implementations5 Aug 2021 Shixiang Feng, YuHang Zhou, Xiaoman Zhang, Ya zhang, Yanfeng Wang

A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network.

Knowledge Distillation

On the Robustness of Domain Adaption to Adversarial Attacks

no code implementations4 Aug 2021 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).

Adversarial Attack Unsupervised Domain Adaptation

Uncertainty-aware Incremental Learning for Multi-organ Segmentation

no code implementations9 Mar 2021 YuHang Zhou, Xiaoman Zhang, Shixiang Feng, Ya zhang, Yanfeng

Specifically, given a pretrained $K$ organ segmentation model and a new single-organ dataset, we train a unified $K+1$ organ segmentation model without accessing any data belonging to the previous training stages.

Ethics Incremental Learning +1

GFL: A Decentralized Federated Learning Framework Based On Blockchain

no code implementations21 Oct 2020 Yifan Hu, YuHang Zhou, Jun Xiao, Chao Wu

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.

Data Poisoning Federated Learning

SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation

no code implementations13 Oct 2020 Xiaoman Zhang, Shixiang Feng, YuHang Zhou, Ya zhang, Yanfeng Wang

We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tumor segmentation.

Brain Tumor Segmentation Self-Supervised Learning +2

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