7 citations · 14 across the 7 of their papers we have counts for
8 papers
XMix: Combating Extremely Noisy Labels via Local Smoothness in Self-Supervised Feature Space
Chengqi Li, Yangdi Lu, Zhihao Shi +3
Supervised deep learning models rely on large, accurately labeled datasets, yet noisy annotations are often unavoidable and can severely degrade performance under high noise levels…
Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian Splatting
Chengqi Li, Zhihao Shi, Yangdi Lu +2
3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors. Existing methods typically rely on heuristic…
Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction
Yangdi Lu, Wenbo He
Deep neural networks has been highly successful in data-intense computer vision applications, while such success relies heavily on the massive and clean data. In real-world scenari…
Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels
Yangdi Lu, Wenbo He
Noisy labels are ubiquitous in real-world datasets, especially in the large-scale ones derived from crowdsourcing and web searching. It is challenging to train deep neural networks…
SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels
Yangdi Lu, Wenbo He
Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-en…
Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Yangdi Lu, Yang Bo, Wenbo He
Recent studies on the memorization effects of deep neural networks on noisy labels show that the networks first fit the correctly-labeled training samples before memorizing the mis…