Feng, Y., Zhu, H., Peng, D., Peng, X., & Hu, P. (2023, October 26). ROAD: Robust Unsupervised Domain Adaptation with Noisy Labels. Proceedings of the 31st ACM International Conference on Multimedia. https://doi.org/10.1145/3581783.3612296
Abstract:
In recent years, Unsupervised Domain Adaptation (UDA) has emerged as a popular technique for transferring knowledge from a labeled source domain to an unlabeled target domain. However, almost all of the existing approaches implicitly assume that the source domain is correctly labeled, which is expensive or even impossible to satisfy in open-world applications due to ubiquitous imperfect annotations (i.e., noisy labels). In this paper, we reveal that noisy labels interfere with learning from the source domain, thus leading to noisy knowledge being transferred from the source domain to the target domain, termed Dual Noisy Information (DNI). To address this issue, we propose a robust unsupervised domain adaptation framework (ROAD), which prevents the network model from overfitting noisy labels to capture accurate discrimination knowledge for domain adaptation. Specifically, a Robust Adaptive Weighted Learning mechanism (RSWL) is proposed to adaptively assign weights to each sample based on its reliability to enforce the model to focus more on reliable samples and less on unreliable samples, thereby mining robust discrimination knowledge against noisy labels in the source domain. In order to prevent noisy knowledge from misleading domain adaptation, we present a Robust Domain-adapted Prediction Learning mechanism (RDPL) to reduce the weighted decision uncertainty of predictions in the target domain, thus ensuring the accurate knowledge of source domain transfer into the target domain, rather than uncertain knowledge from noise impact. Comprehensive experiments are conducted on three widely-used UDA benchmarks to demonstrate the effectiveness and robustness of our ROAD against noisy labels by comparing it with 13 state-of-the-art methods. Code is available at https://github.com/penghu-cs/ROAD.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - MTC Programmatic
Grant Reference no. : A18A2b0046
This research / project is supported by the A*STAR - RobotHTPO
Grant Reference no. : C211518008
This research / project is supported by the Singapore Economic Development Board (EDB) - Space Technology Development Grant (STDP)
Grant Reference no. : S22-19016- STDP
This work was supported in part by the National Natural Science
Foundation of China (U21B2040, U19A2078, 62176171, 61971296,
and 62102274), Sichuan Science and Technology Planning Project
(2023YFQ0020, 2023YFG0033, 2023ZHCG0016, 2022YFQ0014, and
2022YFH0021), Chengdu Science and Technology Project (2023-
XT00-00004-GX), Fundamental Research Funds for the Central Universities (SCU2022JG002 and YJ202140).