1 citations · 1 across the 4 of their papers we have counts for
4 papers
Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition
Xuemei Jia, Jiawei Du, Hui Wei +3
High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory and copyright constraints. Thi…
Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment
Huaiyuan Qin, Muli Yang, Gabriel James Goenawan +5
Existing dynamic data pruning methods often fail under noisy-label settings, as they typically rely on per-sample loss as the ranking criterion. This could mistakenly lead to prese…
Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models
Yuanwei Liu, Chengyu Jia, Ruqi Xiao +4
The task of privacy-preserving face recognition (PPFR) currently faces two major unsolved challenges: (1) existing methods are typically effective only on specific face recognition…
Contributing Dimension Structure of Deep Feature for Coreset Selection
Zhijing Wan, Zhixiang Wang, Yuran Wang +3
Coreset selection seeks to choose a subset of crucial training samples for efficient learning. It has gained traction in deep learning, particularly with the surge in training data…