1 code implementation • 3 Aug 2023 • Lu Zeng, Xuan Chen, Xiaoshuang Shi, Heng Tao Shen
In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise.
no code implementations • 19 Jul 2022 • Xuan Chen, Fei Ji, Miaowen Wen, Yu Huang, Yuankun Tang, Andrew W. Eckford
In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval.
no code implementations • 17 Sep 2020 • Xuan Chen, Zifan Wang, Yucai Fan, Bonan Jin, Piotr Mardziel, Carlee Joe-Wong, Anupam Datta
Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.
no code implementations • 13 Jun 2019 • Hanshu Yan, Xuan Chen, Vincent Y. F. Tan, Wenhan Yang, Joe Wu, Jiashi Feng
They jointly facilitate unsupervised learning of a noise model for various noise types.
no code implementations • ECCV 2018 • Xuan Chen, Jun Hao Liew, Wei Xiong, Chee-Kong Chui, Sim-Heng Ong
In multi-label brain tumor segmentation, class imbalance and inter-class interference are common and challenging problems.