3 papers
cs.CV2026
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…
cs.LG2026
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…
cs.CV2024
Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model
Hang Zhou, Jiale Cai, Yuteng Ye +5
A recent endeavor in one class of video anomaly detection is to leverage diffusion models and posit the task as a generation problem, where the diffusion model is trained to recove…