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Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning by Tian, Lei; Tang, Yongqiang; Hu, Liangchen; Zhang, Wensheng is a scholarly article available to read on EtoBox.

What is Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning about?

Domain adaptation manages to transfer the knowledge of well-labeled source data to unlabeled target data. Many recent efforts focus on improving the prediction accuracy of target pseudo-labels to reduce conditional distribution shift. In this paper, we propose a novel domain adaptation method, which infers target pseudo-labels through cross-domain label propagation, such that the underlying manifold structure of two domain data can be explored. Unlike existing cross-domain label propagation methods that separate domain-invariant feature learning, affinity matrix constructing and target labels inferring into three independent stages, we propose to integrate them into a unified optimization framework. In such way, these three parts can boost each other from an iterative optimization perspective and thus more effective knowledge transfer can be achieved. Furthermore, to construct a high-quality affinity matrix, we propose a discriminative graph self-learning strategy, which can not only adaptively capture the inherent similarity of the data from two domains but also effectively exploit the discriminative information contained in well-labeled source data and pseudo-labeled target data.

Author

Tian, Lei; Tang, Yongqiang; Hu, Liangchen; Zhang, Wensheng

Published

2023

Language

EN