4 citations · 5 across the 4 of their papers we have counts for
4 papers
Multi-network Contrastive Learning Based on Global and Local Representations
Weiquan Li, Xianzhong Long, Yun Li
The popularity of self-supervised learning has made it possible to train models without relying on labeled data, which saves expensive annotation costs. However, most existing self…
MSVQ: Self-Supervised Learning with Multiple Sample Views and Queues
Chen Peng, Xianzhong Long, Yun Li
Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer…
Synthetic Hard Negative Samples for Contrastive Learning
Hengkui Dong, Xianzhong Long, Yun Li +1
Contrastive learning has emerged as an essential approach for self-supervised learning in visual representation learning. The central objective of contrastive learning is to maximi…
Learning Task-aware Robust Deep Learning Systems
Keji Han, Yun Li, Xianzhong Long +1
Many works demonstrate that deep learning system is vulnerable to adversarial attack. A deep learning system consists of two parts: the deep learning task and the deep model. Nowad…