I am a Ph.D. student at University of California, Santa Cruz (UCSC), working with Professor Yang Liu. I am currently interested in weakly supervised learning (learning with imperfect information) and explainable neural networks (network understanding). Previously, I spent two years at YouTu Lab, Tencent, working with Ke Li and Xing Sun.
Two papers are available on arxiv: RMNet and SSL for Noisy Label.
One paper accepted to ICLR 2021.
One paper accepted to NeurlPS 2020.
One paper accepted to CVPR 2020.
Selected Publication (* denotes equal contribution)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR 2021)
Hao cheng*, Zhaowei Zhu*, Xingyu Li, Yifei Gong, Xing Sun, Yang Liu.
Pruning Filter in Filter (NeurlPS 2020)
Fanxu Meng*, Hao Cheng*, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun.
Filter Grafting for Deep Neural Networks (CVPR 2020)
Fanxu Meng*, Hao Cheng*, Ke Li, Zhixin Xu, Rongrong Ji, Xing Sun, Guangming Lu.
Local to Global Learning: Gradually Adding Classes for Training Deep Neural Networks (CVPR 2019)
Hao Cheng*, Dongze Lian*, Bowen Deng, Shenghua Gao, Tao Tan, Yanlin Geng.
Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane (ECCV 2018)
Hao Cheng, Dongze Lian, Shenghua Gao, Yanlin Geng.
Invited talk by Stan Z. Li on Mutual Information in Deep Learning.
Invited talk by 机器之心 on Learning with Noisy Labels and Collaborative Learning.
Chancellor Fellowship UCSC 2021
Four-Star Distinguished Staff Youtu Lab, Tencent 2020
Technology Breakthrough Award Youtu Lab, Tencent 2020
Top-1 Lecturer of the Year Youtu Lab, Tencent 2020