5 citations · 6 across the 7 of their papers we have counts for
7 papers
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…
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…