no code implementations • 30 May 2024 • Guogang Zhu, Xuefeng Liu, Xinghao Wu, Shaojie Tang, Chao Tang, Jianwei Niu, Hao Su
Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towards some certain classes.
1 code implementation • 20 Mar 2024 • Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu
Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization.
1 code implementation • ICCV 2023 • Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Shaojie Tang
The reasoning behind this approach is understandable, as localizing parameters that are easily influenced by non-IID data can prevent the potential negative effect of collaboration.
no code implementations • 26 Jul 2023 • Guogang Zhu, Xuefeng Liu, Shaojie Tang, Jianwei Niu, Xinghao Wu, Jiaxing Shen
FedPick achieves PFL in the low-dimensional feature space by selecting task-relevant features adaptively for each client from the features generated by the global encoder based on its local data distribution.
no code implementations • 19 Jul 2023 • Hao Su, Xuefeng Liu, Jianwei Niu, Ji Wan, Xinghao Wu
Unlike these studies, our 3Deformer is a non-training and common framework, which only requires supervision of readily-available semantic images, and is compatible with editing various objects unlimited by datasets.