4 citations · 6 across the 5 of their papers we have counts for
5 papers
Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation
Xiaona Sun, Zhenyu Wu, Zhiqiang Zhan +1
Many existing unsupervised domain adaptation (UDA) methods primarily focus on covariate shift, limiting their effectiveness in imbalanced domain adaptation (IDA) where both covaria…
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics
Zhuoxin Chen, Zhenyu Wu, Yang Ji
Federated learning is designed to enhance data security and privacy, but faces challenges when dealing with heterogeneous data in long-tailed and non-IID distributions. This paper…
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Xiaona Sun, Zhenyu Wu, Yichen Liu +3
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that…
Dual-Branch Temperature Scaling Calibration for Long-Tailed Recognition
Jialin Guo, Zhenyu Wu, Zhiqiang Zhan +1
The calibration for deep neural networks is currently receiving widespread attention and research. Miscalibration usually leads to overconfidence of the model. While, under the con…
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
Wenfang Lin, Zhenyu Wu, Yang Ji
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it become…