NettetWe consider learning a multi-class classification model in the federated setting, where each user has access to the positive data associated with only a single class. As a result, during each federated learning round, the users need to locally update the classifier without having access to the features and the model parameters for the negative classes. Nettet15. mar. 2024 · Federated learning with only positive labels. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR (2024), pp. 10946-10956. View in Scopus Google Scholar [68] Khodak M., Balcan M.-F.F., Talwalkar A.S. Adaptive gradient-based meta-learning methods.

arXiv:2004.10342v1 [cs.LG] 21 Apr 2024

NettetA list of papers on Federated Deep Learning in Healthcare, in particular, algorithms Deep Learning with Medical Imaging. ... FedAwS: Federated Learning with Only Positive … Nettetlearning positive label correlations [6], performing label matrix completion [4], or learning to infer missing labels [54] break down in the single positive only setting. We direct attention to this important but underexplored variant of multi-label learning. Our experiments show that training with a single positive label per image allows us old school zydeco

【联邦学习论文分享01】FL with Only Positive Labels - 知乎

NettetWe consider an extreme of this weakly supervised learning problem, called single positive multi-label learning (SPML), where each multi-label training image has only one positive label. Traditionally, all unannotated labels are assumed as negative labels in SPML, which introduces false negative labels and causes model training to be … Nettetlearning positive label correlations [6], performing label matrix completion [4], or learning to infer missing labels [54] break down in the single positive only setting. We direct … Nettetlabel of every unlabeled example. PLL aims to learn from ambiguous labeling information where each training exam-ple is associated with a set of candidate labels, among which only one label is valid[Couret al., 2011; Gonget al., 2024; Feng and An, 2024; Chenet al., 2024]. Recent successful PLL methods have devised various disambiguation regulariz- old school θυρα 7