5 citations · 14 across the 14 of their papers we have counts for
14 papers
Arena: A Learning-based Synchronization Scheme for Hierarchical Federated Learning--Technical Report
Tianyu Qi, Yufeng Zhan, Peng Li +2
Federated learning (FL) enables collaborative model training among distributed devices without data sharing, but existing FL suffers from poor scalability because of global model s…
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models
Ruibin Li, Qihua Zhou, Song Guo +5
Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resol…
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition
Jingcai Guo, Yuanyuan Xu, Wenchao Xu +3
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mo…
Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning
Yingchun Wang, Jingcai Guo, Song Guo +2
Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model…
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning
Yingchun Wang, Song Guo, Jingcai Guo +4
Network pruning is a promising way to generate light but accurate models and enable their deployment on resource-limited edge devices. However, the current state-of-the-art assumes…
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming
Qihua Zhou, Ruibin Li, Song Guo +4
Recent years have witnessed the dramatic growth of Internet video traffic, where the video bitstreams are often compressed and delivered in low quality to fit the streamer's uplink…
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network
Jingcai Guo, Song Guo, Jie Zhang +1
Federated learning (FL) has emerged as a promising privacy-preserving distributed machine learning framework recently. It aims at collaboratively learning a shared global model by…
Personalized Federated Learning with Contextualized Generalization
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 trainin…
A Novel Perspective to Zero-shot Learning: Towards an Alignment of Manifold Structures via Semantic Feature Expansion
Jingcai Guo, Song Guo
Zero-shot learning aims at recognizing unseen classes (no training example) with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic featur…
AMS-SFE: Towards an Alignment of Manifold Structures via Semantic Feature Expansion for Zero-shot Learning
Jingcai Guo, Song Guo
Zero-shot learning (ZSL) aims at recognizing unseen classes with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic feature space (FS) sha…
MAANet: Multi-view Aware Attention Networks for Image Super-Resolution
Jingcai Guo, Shiheng Ma, Song Guo
In most recent years, deep convolutional neural networks (DCNNs) based image super-resolution (SR) has gained increasing attention in multimedia and computer vision communities, fo…
Position-Aware Convolutional Networks for Traffic Prediction
Shiheng Ma, Jingcai Guo, Song Guo +1
Forecasting the future traffic flow distribution in an area is an important issue for traffic management in an intelligent transportation system. The key challenge of traffic predi…
EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach
Jingcai Guo, Song Guo
Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which i…
Adaptive Adjustment with Semantic Feature Space for Zero-Shot Recognition
Jingcai Guo, Song Guo
In most recent years, zero-shot recognition (ZSR) has gained increasing attention in machine learning and image processing fields. It aims at recognizing unseen class instances wit…