Shuaicheng Niu

Online Adaptive Asymmetric Active Learning with Limited Budgets

2019 · arXiv (Cornell University)

Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there …

Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment

2023 · arXiv (Cornell University)

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a well-trained source model to an unlabeled target domain without access to the source data. One key challenge is the lack of source data during domain adaptation. …