3 citations · 3 across the 5 of their papers we have counts for
5 papers
Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning
Chen Shu, Mengke Li, Yiqun Zhang +4
In real-world datasets, the challenges of long-tailed distributions and noisy labels often coexist, posing obstacles to the model training and performance. Existing studies on long…
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
Zhenheng Tang, Yonggang Zhang, Peijie Dong +4
One-shot Federated Learning (OFL) significantly reduces communication costs in FL by aggregating trained models only once. However, the performance of advanced OFL methods is far b…
Exploiting Counter-Examples for Active Learning with Partial labels
Fei Zhang, Yunjie Ye, Lei Feng +6
This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle…
Label-Noise Learning with Intrinsically Long-Tailed Data
Yang Lu, Yiliang Zhang, Bo Han +2
Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth class…
AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +2
Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have been designed to reason on knowledge graphs (KGs). An important design component of GNN-based…