Search Results for author: Chao Wu

Found 27 papers, 8 papers with code

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

no code implementations20 Jun 2022 Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu

To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.

Multi-agent Reinforcement Learning Starcraft +1

Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

1 code implementation CVPR 2022 Chao Wu, Wenhang Ge, AnCong Wu, Xiaobin Chang

To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role.

Person Re-Identification

Mining Latent Relationships among Clients: Peer-to-peer Federated Learning with Adaptive Neighbor Matching

no code implementations23 Mar 2022 Zexi Li, Jiaxun Lu, Shuang Luo, Didi Zhu, Yunfeng Shao, Yinchuan Li, Zhimeng Zhang, Chao Wu

Moreover, results show that our method is much effective in mining latent cluster relationships under various heterogeneity without assuming the number of clusters and it is effective even under low communication budgets.

Federated Learning

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning

no code implementations28 Jan 2022 Jie Zhang, Lei Zhang, Gang Li, Chao Wu

Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes.

Towards Efficient Data Free Black-Box Adversarial Attack

no code implementations CVPR 2022 Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, Chao Wu

The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.

Adversarial Attack

A Practical Data-Free Approach to One-shot Federated Learning with Heterogeneity

no code implementations23 Dec 2021 Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Jianghe Xu, Shouhong Ding, Chao Wu

To the best of our knowledge, FedSyn is the first method that can be practically applied to various real-world applications due to the following advantages: (1) FedSyn requires no additional information (except the model parameters) to be transferred between clients and the server; (2) FedSyn does not require any auxiliary dataset for training; (3) FedSyn is the first to consider both model and statistical heterogeneities in FL, i. e., the clients' data are non-iid and different clients may have different model architectures.

Federated Learning

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.

Edge-computing

Unified Group Fairness on Federated Learning

no code implementations9 Nov 2021 Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao

We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.

Fairness Federated Learning

Ensemble Federated Adversarial Training with Non-IID data

no code implementations26 Oct 2021 Shuang Luo, Didi Zhu, Zexi Li, Chao Wu

Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.

Federated Learning

Federated Graph Learning -- A Position Paper

no code implementations24 May 2021 Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, Chao Wu

Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training.

Federated Learning Graph Learning

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent

no code implementations15 Apr 2021 Zhao Wang, Yifan Hu, Jun Xiao, Chao Wu

A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization.

Federated Learning

Verifying Design through Generative Visualization of Neural Activities

no code implementations28 Mar 2021 Pan Wang, Danlin Peng, Simiao Yu, Chao Wu, Peter Childs, Yike Guo, Ling Li

A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.

EEG

GFL: A Decentralized Federated Learning Framework Based On Blockchain

no code implementations21 Oct 2020 Yifan Hu, YuHang Zhou, Jun Xiao, Chao Wu

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.

Data Poisoning Federated Learning

Federated Unsupervised Representation Learning

no code implementations18 Oct 2020 Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, Xiaolin Li

FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.

Federated Learning Representation Learning

Federated Mutual Learning

1 code implementation27 Jun 2020 Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu

The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Federated Learning

Evaluation Framework For Large-scale Federated Learning

1 code implementation3 Mar 2020 Lifeng Liu, Fengda Zhang, Jun Xiao, Chao Wu

Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy.

Federated Learning

Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach

no code implementations6 Feb 2020 Zeyue Xue, Shuang Luo, Chao Wu, Pan Zhou, Kaigui Bian, Wei Du

Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.

Transfer Learning

Galaxy Learning -- A Position Paper

no code implementations22 Apr 2019 Chao Wu, Jun Xiao, Gang Huang, Fei Wu

Model training, as well as the communication, is achieved with blockchain and its smart contracts.

Machine Learning

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

1 code implementation13 Jun 2018 Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.

Traffic Prediction

Generative Creativity: Adversarial Learning for Bionic Design

no code implementations19 May 2018 Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo

Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

no code implementations20 Nov 2017 Hao Dong, Chao Wu, Zhen Wei, Yike Guo

However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.

Anomaly Detection Decision Making +1

Semantic Image Synthesis via Adversarial Learning

2 code implementations ICCV 2017 Hao Dong, Simiao Yu, Chao Wu, Yike Guo

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.

Image Generation Image Manipulation

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

7 code implementations12 Mar 2017 Akara Supratak, Hao Dong, Chao Wu, Yike Guo

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

EEG Sleep Stage Detection

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

no code implementations10 Jan 2017 Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo

It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.

Translation Unsupervised Image-To-Image Translation

Mixed Neural Network Approach for Temporal Sleep Stage Classification

no code implementations15 Oct 2016 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo

Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Classification EEG +1

Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge

no code implementations21 Sep 2015 Xiaofei Wang, Chao Wu, Pengyuan Zhang, Ziteng Wang, Yong liu, Xu Li, Qiang Fu, Yonghong Yan

This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.

Automatic Speech Recognition speech-recognition +1

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