no code implementations • 13 Apr 2024 • Rongguang Ye, Ming Tang
Fairness in federated learning has emerged as a critical concern, aiming to develop an unbiased model for any special group (e. g., male or female) of sensitive features.
no code implementations • 12 Apr 2024 • Rongguang Ye, Lei Chen, Weiduo Liao, Jinyuan Zhang, Hisao Ishibuchi
In this manner, the proposed method can sample preference vectors from the location of the Pareto front with a high probability.
2 code implementations • 12 Apr 2024 • Rongguang Ye, Longcan Chen, Jinyuan Zhang, Hisao Ishibuchi
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network.
1 code implementation • 1 Apr 2024 • Chikai Shang, Rongguang Ye, Jiaqi Jiang, Fangqing Gu
In this paper, we propose a Collaborative Pareto Set Learning (CoPSL) framework, which simultaneously learns the Pareto sets of multiple MOPs in a collaborative manner.
1 code implementation • 12 Apr 2022 • Zhaohui Zheng, Rongguang Ye, Qibin Hou, Dongwei Ren, Ping Wang, WangMeng Zuo, Ming-Ming Cheng
Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and the absence of localization distillation is a critical reason for why logit mimicking underperforms for years.
2 code implementations • CVPR 2022 • Zhaohui Zheng, Rongguang Ye, Ping Wang, Dongwei Ren, WangMeng Zuo, Qibin Hou, Ming-Ming Cheng
Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.
6 code implementations • 7 May 2020 • Zhaohui Zheng, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, QinGhua Hu, WangMeng Zuo
In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
20 code implementations • 19 Nov 2019 • Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, Dongwei Ren
By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e. g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric.