535 citations · 587 across the 10 of their papers we have counts for
5 papers · 1 filter
Learning from Noisy Labels with Decoupled Meta Label Purifier
Yuanpeng Tu, Boshen Zhang, Yuxi Li +5
Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-lear…
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Yuanpeng Tu, Boshen Zhang, Yuxi Li +6
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithm…
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes
Yuanpeng Tu, Yuxi Li, Boshen Zhang +4
Robust autonomous driving requires agents to accurately identify unexpected areas (anomalies) in urban scenes. To this end, some critical issues remain open: how to design advisabl…
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
Boshen Zhang, Yuxi Li, Yuanpeng Tu +5
Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to…
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
Zhengkai Jiang, Yuxi Li, Ceyuan Yang +4
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Ada…