Importance-weighted conditional adversarial network for unsupervised domain adaptation. - Peng Liu 008; Ting Xiao; Cangning Fan; Wei Zhao 008; Xianglong Tang; Hongwei Liu 002 (Expert Syst. Appl., 2020)

Domain adaptation based on domain-invariant and class-distinguishable feature learning using multiple adversarial networks. - Cangning Fan; Peng Liu 008; Ting Xiao; Wei Zhao 008; Xianglong Tang (Neurocomputing, 2020)

Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection. - Cangning Fan; Fangyi Zhang; Peng Liu; Xiuyu Sun; Hao Li 030; Ting Xiao; Wei Zhao; Xianglong Tang (CoRR, 2021)

Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation. - Ting Xiao; Cangning Fan; Peng Liu 008; Hongwei Liu 002 (Entropy, 2022)

Transferable visual pattern memory network for domain adaptation in anomaly detection. - Cangning Fan; Ye Jin; Peng Liu; Wei Zhao (Eng. Appl. Artif. Intell., 2023)

Curriculum adaptation method based on graph neural networks for universal domain adaptation. - Cangning Fan; Peng Liu 008; Wei Zhao 008 (Expert Syst. Appl., 2024)

Mahalanobis distance-guided conditional adversarial learning for universal domain adaptation. - Cangning Fan; Peng Liu; Wei Zhao (Knowl. Based Syst., 2025)