Jianfei Yang

Advancing Imbalanced Domain Adaptation: Cluster-Level Discrepancy Minimization With a Comprehensive Benchmark

2021 · IEEE Transactions on Cybernetics

Unsupervised domain adaptation methods have been proposed to tackle the problem of covariate shift by minimizing the distribution discrepancy between the feature embeddings of source domain and target domain. However, the standard evaluation protocols assume …

Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation

2022 · arXiv (Cornell University)

Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models. Although existing UDA …

Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation

2022 · arXiv (Cornell University)

Assuming the source label space subsumes the target one, Partial Video Domain Adaptation (PVDA) is a more general and practical scenario for cross-domain video classification problems. The key challenge of PVDA is to mitigate the …

Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels

2022 · arXiv (Cornell University)

Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and …

SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain. Though achieving good performance, …