7 papers
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
Zhuo Huang, Qizhou Wang, Ziming Hong +3
For ethical and safe AI, machine unlearning rises as a critical topic aiming to protect sensitive, private, and copyrighted knowledge from misuse. To achieve this goal, it is commo…
Intellectual Property Protection for 3D Gaussian Splatting Assets: A Survey
Longjie Zhao, Ziming Hong, Jiaxin Huang +3
3D Gaussian Splatting (3DGS) has become a mainstream representation for real-time 3D scene synthesis, enabling applications in virtual and augmented reality, robotics, and 3D conte…
AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing
Ziming Hong, Tianyu Huang, Runnan Chen +4
Recent studies have extended diffusion-based instruction-driven 2D image editing pipelines to 3D Gaussian Splatting (3DGS), enabling faithful manipulation of 3DGS assets and greatl…
Prototype-Guided Curriculum Learning for Zero-Shot Learning
Lei Wang, Shiming Chen, Guo-Sen Xie +4
In Zero-Shot Learning (ZSL), embedding-based methods enable knowledge transfer from seen to unseen classes by learning a visual-semantic mapping from seen-class images to class-lev…
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Ziming Hong, Runnan Chen, Zengmao Wang +3
Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator…
Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising
Yongli Xiang, Ziming Hong, Lina Yao +2
Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to…