1 code implementation • 2 Jun 2023 • Yun Chu, Pu Li, Yong Bai, Zhuhua Hu, Yongqing Chen, Jiafeng Lu
To address these issues, for the object detection network we introduce a three-stage model compression method: dynamic sparse training, group channel pruning, and spatial attention distilling.
1 code implementation • 22 May 2023 • Zhangming Chan, Yu Zhang, Shuguang Han, Yong Bai, Xiang-Rong Sheng, Siyuan Lou, Jiacen Hu, Baolin Liu, Yuning Jiang, Jian Xu, Bo Zheng
However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions.
no code implementations • 5 Jul 2022 • Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou
In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not.
no code implementations • 5 Mar 2020 • Yong Bai, Yuanfang Guo, Jinjie Wei, Lin Lu, Rui Wang, Yunhong Wang
With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts.