no code implementations • 24 Nov 2021 • Yujie Wang, Junqin Huang, Mengya Gao, Yichao Wu, Zhenfei Yin, Ding Liang, Junjie Yan
Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.
no code implementations • 16 Nov 2021 • Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
no code implementations • 31 Mar 2021 • Jiangfan Han, Mengya Gao, Yujie Wang, Quanquan Li, Hongsheng Li, Xiaogang Wang
To solve this problem, in this paper, we propose a novel student-dependent distillation method, knowledge consistent distillation, which makes teacher's knowledge more consistent with the student and provides the best suitable knowledge to different student networks for distillation.
no code implementations • 21 Feb 2020 • Mengya Gao, Yujun Shen, Quanquan Li, Chen Change Loy
Knowledge distillation (KD) is one of the most potent ways for model compression.
no code implementations • CVPR 2019 • Xiao Zhang, Rui Zhao, Junjie Yan, Mengya Gao, Yu Qiao, Xiaogang Wang, Hongsheng Li
Cosine-based softmax losses significantly improve the performance of deep face recognition networks.
1 code implementation • 5 Dec 2018 • Mengya Gao, Yujun Shen, Quanquan Li, Junjie Yan, Liang Wan, Dahua Lin, Chen Change Loy, Xiaoou Tang
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model.
1 code implementation • 17 Jun 2018 • C. -H. Huck Yang, Jia-Hong Huang, Fangyu Liu, Fang-Yi Chiu, Mengya Gao, Weifeng Lyu, I-Hung Lin M. D., Jesper Tegner
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.