3 papers
cs.LG2026
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…
cs.CV2026
LDP-Slicing: Local Differential Privacy for Images via Randomized Bit-Plane Slicing
Yuanming Cao, Chengqi Li, Wenbo He
Local Differential Privacy (LDP) is the gold standard trust model for privacy-preserving machine learning by guaranteeing privacy at the data source. However, its application to im…
cs.CV2025
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…