9 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…
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…
Towards Generalized Multi-Image Editing for Unified Multimodal Models
Pengcheng Xu, Peng Tang, Donghao Luo +7
Unified Multimodal Models (UMMs) integrate multimodal understanding and generation, yet they are limited to maintaining visual consistency and disambiguating visual cues when refer…
Event-Driven Online Vertical Federated Learning
Ganyu Wang, Boyu Wang, Bin Gu +1
Online learning is more adaptable to real-world scenarios in Vertical Federated Learning (VFL) compared to offline learning. However, integrating online learning into VFL presents…
MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge
Maxwell J. Yin, Boyu Wang, Charles Ling
Models trained on real-world data often mirror and exacerbate existing social biases. Traditional methods for mitigating these biases typically require prior knowledge of the speci…
Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing
Pengcheng Xu, Boyuan Jiang, Xiaobin Hu +7
Leveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible…