11 citations · 26 across the 7 of their papers we have counts for
9 papers
Attention Diversification for Domain Generalization
Rang Meng, Xianfeng Li, Weijie Chen +7
Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are…
Dynamic Domain Generalization
Zhishu Sun, Zhifeng Shen, Luojun Lin +4
Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limit…
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning
Yongfeng Li, Mingming Zhao, Weijie Chen +1
In this paper, we consider the linear programming (LP) formulation for deep reinforcement learning. The number of the constraints depends on the size of state and action spaces, wh…
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Weijie Chen, Luojun Lin, Shicai Yang +4
It is a strong prerequisite to access source data freely in many existing unsupervised domain adaptation approaches. However, source data is agnostic in many practical scenarios du…
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Weijie Chen, Yilu Guo, Shicai Yang +7
False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countles…
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
Xianfeng Li, Weijie Chen, Di Xie +4
Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the…