8 papers
Full spectrum Unlearnable Examples via Spectral Equalization
Jiale Cai, Gezheng Xu, Zhihao Li +6
Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that…
CFPO: Counterfactual Policy Optimization for Multimodal Reasoning
Zhangyuan Yu, Wanran Sun, Guangjing Yang +2
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal reasoning. However, prevailing reinforcement learning (RL) paradigms lack explicit coun…
Stabilized Fine-Tuning with LoRA in Federated Learning: Mitigating the Side Effect of Client Size and Rank via the Scaling Factor
Jiayu Huang, Xiaohu Wu, Tiantian He +1
Large Language Models (LLMs) are pivotal in natural language processing. The impracticality of full fine-tuning has prompted Parameter-Efficient Fine-Tuning (PEFT) methods like Low…
When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining
Zhihao Li, Gezheng Xu, Jiale Cai +5
Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations instead of underlyin…
iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection
Huahui Yi, Wei Xu, Ziyuan Qin +4
Existing prompt-based approaches have demonstrated impressive performance in continual learning, leveraging pre-trained large-scale models for classification tasks; however, the ti…
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Mengmeng Chen, Xiaohu Wu, Qiqi Liu +5
Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for…