13 citations · 43 across the 16 of their papers we have counts for
10 papers · 1 filter
DPSformer: A long-tail-aware model for improving heavy rainfall prediction
Zenghui Huang, Ting Shu, Zhonglei Wang +4
Accurate and timely forecasting of heavy rainfall remains a critical challenge for modern society. Precipitation exhibits a highly imbalanced distribution: most observations record…
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…
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
Shihao Hou, Xinyi Shang, Shreyank N Gowda +4
Effectively handling the co-occurrence of non-IID data and long-tailed distributions remains a critical challenge in federated learning. While fine-tuning vision-language models (V…
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data
Shanshan Yan, Zexi Li, Chao Wu +4
Data heterogeneity, stemming from local non-IID data and global long-tailed distributions, is a major challenge in federated learning (FL), leading to significant performance gaps…
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Chenxing Hong, Yan Jin, Zhiqi Kang +4
Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. I…
Federated Learning with Extremely Noisy Clients via Negative Distillation
Yang Lu, Lin Chen, Yonggang Zhang +4
Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noi…