Search Results for author: Song Guo

Found 27 papers, 3 papers with code

CE-based white-box adversarial attacks will not work using super-fitting

no code implementations4 May 2022 Youhuan Yang, Lei Sun, Leyu Dai, Song Guo, Xiuqing Mao, Xiaoqin Wang, Bayi Xu

This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i. e., the accuracy under adversarial attacks).

Adversarial Attack Adversarial Robustness

Rethinking Classifier and Adversarial Attack

no code implementations4 May 2022 Youhuan Yang, Lei Sun, Leyu Dai, Song Guo, Xiuqing Mao, Xiaoqin Wang, Bayi Xu

Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i. e., not approaching the lower bound of robustness).

Adversarial Attack Adversarial Robustness

Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression

no code implementations14 Apr 2022 Feijie Wu, Shiqi He, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang, Jie Zhang

Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performance computing systems such as public clouds.

Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention

no code implementations7 Mar 2022 Ziming Liu, Song Guo, Jingcai Guo, Yuanyuan Xu, Fushuo Huo

We argue that disregarding the connection between major and minor classes, i. e., correspond to the global and local information, respectively, is the cause of the problem.

Multi-label zero-shot learning

Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations

no code implementations27 Feb 2022 Tao Guo, Song Guo, Jiewei Zhang, Wenchao Xu, Junxiao Wang

Existing studies of machine unlearning mainly focus on sample-wise unlearning, such that a learnt model will not expose user's privacy at the sample level.

Face Recognition Fairness

A Coalition Formation Game Approach for Personalized Federated Learning

no code implementations5 Feb 2022 Leijie Wu, Song Guo, Yaohong Ding, Yufeng Zhan, Jie Zhang

Facing the challenge of statistical diversity in client local data distribution, personalized federated learning (PFL) has become a growing research hotspot.

Personalized Federated Learning

From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization

no code implementations17 Dec 2021 Feijie Wu, Song Guo, Haozhao Wang, Zhihao Qu, Haobo Zhang, Jie Zhang, Ziming Liu

In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training with fully utilizing their computational resources.

Parameterized Knowledge Transfer for Personalized Federated Learning

no code implementations NeurIPS 2021 Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wencao Xu, Feijie Wu

To deal with such model constraints, we exploit the potentials of heterogeneous model settings and propose a novel training framework to employ personalized models for different clients.

Personalized Federated Learning Transfer Learning

Federated Unlearning via Class-Discriminative Pruning

no code implementations22 Oct 2021 Junxiao Wang, Song Guo, Xin Xie, Heng Qi

Evaluated on CIFAR10 dataset, our method accelerates the speed of unlearning by 8. 9x for the ResNet model, and 7. 9x for the VGG model under no degradation in accuracy, compared to retraining from scratch.

Federated Learning Image Classification

SDN-based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach

no code implementations26 Sep 2021 Jun Du, Chunxiao Jiang, Abderrahim Benslimane, Song Guo, Yong Ren

Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs).

Edge-computing

Personalized Federated Learning with Contextualized Generalization

no code implementations24 Jun 2021 Xueyang Tang, Song Guo, Jingcai Guo

The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models.

Personalized Federated Learning

Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities

no code implementations15 Apr 2021 Yuben Qu, Haipeng Dai, Yan Zhuang, Jiafa Chen, Chao Dong, Fan Wu, Song Guo

Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields.

Federated Learning

Online Multiple Object Tracking with Cross-Task Synergy

1 code implementation CVPR 2021 Song Guo, Jingya Wang, Xinchao Wang, DaCheng Tao

On the other hand, such reliable embeddings can boost identity-awareness through memory aggregation, hence strengthen attention modules and suppress drifts.

Frame Multiple Object Tracking

DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels

1 code implementation25 Sep 2020 Song Guo

2) The segmentation speed of DPN is over 20-160 times faster than other methods on the DRIVE dataset.

Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework

no code implementations10 Aug 2020 Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu, Song Guo, Yang Zhang, Dusit Niyato, Cyril Leung, Chunyan Miao

This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation.

Cryptography and Security

A Novel Perspective to Zero-shot Learning: Towards an Alignment of Manifold Structures via Semantic Feature Expansion

no code implementations30 Apr 2020 Jingcai Guo, Song Guo

One common practice in zero-shot learning is to train a projection between the visual and semantic feature spaces with labeled seen classes examples.

Zero-Shot Learning

Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases

no code implementations3 Feb 2020 Huawei Huang, Kangying Lin, Song Guo, Pan Zhou, Zibin Zheng

In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates.

Federated Learning reinforcement-learning

Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning

no code implementations22 Jan 2020 Haozhao Wang, Zhihao Qu, Song Guo, Xin Gao, Ruixuan Li, Baoliu Ye

A major bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) algorithm for large-scale Federated Learning is the communication overhead on pushing local gradients and pulling global model.

Federated Learning

PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks

no code implementations17 Dec 2019 Sicong Zhou, Huawei Huang, Wuhui Chen, Zibin Zheng, Song Guo

Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE.

Distributed, Parallel, and Cluster Computing Cryptography and Security

Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions

no code implementations21 Apr 2019 Hongji Huang, Song Guo, Guan Gui, Zhen Yang, Jianhua Zhang, Hikmet Sari, Fumiyuki Adachi

The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications.

MAANet: Multi-view Aware Attention Networks for Image Super-Resolution

no code implementations12 Apr 2019 Jingcai Guo, Shiheng Ma, Song Guo

Specifically, we propose the local aware (LA) and global aware (GA) attention to deal with LR features in unequal manners, which can highlight the high-frequency components and discriminate each feature from LR images in the local and the global views, respectively.

Image Super-Resolution

Position-Aware Convolutional Networks for Traffic Prediction

no code implementations12 Apr 2019 Shiheng Ma, Jingcai Guo, Song Guo, Minyi Guo

Our approach employs the inception backbone network to capture rich features of traffic distribution on the whole area.

Traffic Prediction

Adaptive Adjustment with Semantic Feature Space for Zero-Shot Recognition

no code implementations30 Mar 2019 Jingcai Guo, Song Guo

To the best of our knowledge, our work is the first to consider the adaptive adjustment of semantic FS in ZSR.

Zero-Shot Learning

EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach

no code implementations30 Mar 2019 Jingcai Guo, Song Guo

In order to deal with this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature learning approach to improve the conventional AE.

Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training

no code implementations21 Feb 2019 Chengjie Li, Ruixuan Li, Haozhao Wang, Yuhua Li, Pan Zhou, Song Guo, Keqin Li

Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models.

BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation

1 code implementation11 Mar 2018 Song Guo, Kai Wang, Hong Kang, Yujun Zhang, Yingqi Gao, Tao Li

Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods.

Retinal Vessel Segmentation

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