activity
20212023
most citedLatent Heterogeneous Graph Network for Incomplete Multi-View Learning

71 citations · 99 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"

Junbo Zhao, Xuefei Ning, Enshu Liu +7

Predictor-based Neural Architecture Search (NAS) employs an architecture performance predictor to improve the sample efficiency. However, predictor-based NAS suffers from the sever…

cs.LG202271 cited

Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

Pengfei Zhu, Xinjie Yao, Yu Wang +4

Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications f…

cs.CV2022

Class-Specific Semantic Reconstruction for Open Set Recognition

Hongzhi Huang, Yu Wang, Qinghua Hu +1

Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existin…

cs.CV202228 cited

Multi-Granularity Regularized Re-Balancing for Class Incremental Learning

Huitong Chen, Yu Wang, Qinghua Hu

Deep learning models suffer from catastrophic forgetting when learning new tasks incrementally. Incremental learning has been proposed to retain the knowledge of old classes while…

cs.CV2021

United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks

Sindi Shkodrani, Yu Wang, Marco Manfredi +1

Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification. Though ways of best harvesting learning improvements from hi…