48 citations · 71 across the 7 of their papers we have counts for
7 papers
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…
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
Mengke Li, Zhikai Hu, Yang Lu +3
The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head clas…
PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition
Xinwen Fan, Yukang Zhang, Yang Lu +1
Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis. Extracting robust feature re…
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Yang Lu, Pinxin Qian, Gang Huang +1
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner. Existing PFL metho…
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…
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Xinyi Shang, Yang Lu, Gang Huang +1
Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is…
Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem
Yang Lu, Yiu-ming Cheung, Yuan Yan Tang
Recent studies have shown that imbalance ratio is not the only cause of the performance loss of a classifier in imbalanced data classification. In fact, other data factors, such as…