no code implementations • 12 Jan 2023 • Yilu Guo, Xingyue Shi, WeiJie Chen, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang
In the test-time training stage, we use the pre-trained model to assign noisy label for the unlabeled target data, and propose a Label-Periodically-Updated DivideMix method for noisy label learning.
no code implementations • 13 Jun 2022 • Yilu Guo, Shicai Yang, WeiJie Chen, Liang Ma, Di Xie, ShiLiang Pu
Therefore, it is crucial to study how to learn more discriminative representations while avoiding over-fitting.
no code implementations • 1 Feb 2021 • WeiJie Chen, Yilu Guo, Shicai Yang, Zhaoyang Li, Zhenxin Ma, Binbin Chen, Long Zhao, Di Xie, ShiLiang Pu, Yueting Zhuang
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
1 code implementation • 20 Jun 2020 • Wei-Jie Chen, ShiLiang Pu, Di Xie, Shicai Yang, Yilu Guo, Luojun Lin
Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
no code implementations • 23 Jul 2019 • Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille
Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes.
2 code implementations • CVPR 2018 • Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille
Age estimation from facial images is typically cast as a nonlinear regression problem.
Ranked #6 on Age Estimation on FGNET
no code implementations • NeurIPS 2017 • Wei Shen, Kai Zhao, Yilu Guo, Alan Yuille
This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.
Ranked #11 on Age Estimation on MORPH album2 (Caucasian)