no code implementations • 20 Apr 2024 • Xiaofei Wang, Yunfeng Zhao, Chao Qiu, QinGhua Hu, Victor C. M. Leung
In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI.
no code implementations • 14 Dec 2023 • Xiaoqiang Gui, Yueyao Cheng, Xiang-Rong Sheng, Yunfeng Zhao, Guoxian Yu, Shuguang Han, Yuning Jiang, Jian Xu, Bo Zheng
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
no code implementations • 9 Aug 2023 • Yunfeng Zhao, Xu Yan, Xiaoqiang Gui, Shuguang Han, Xiang-Rong Sheng, Guoxian Yu, Jufeng Chen, Zhao Xu, Bo Zheng
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
1 code implementation • 8 Sep 2022 • Yunfeng Zhao, Stuart Ferguson, Huiyu Zhou, Karen Rafferty
In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed.
no code implementations • 30 Dec 2021 • Yunfeng Zhao, Stuart Ferguson, Huiyu Zhou, Chris Elliott, Karen Rafferty
This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms.
no code implementations • 2 Jun 2021 • Yunfeng Zhao, Guoxian Yu, Lei Liu, Zhongmin Yan, Lizhen Cui, Carlotta Domeniconi
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label.
1 code implementation • 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2018 • Yunfeng Zhao, Huiyu Zhou, Chris Elliott, Karen Rafferty
Achieving color constancy between and within images, i. e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition.