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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
Fair Dataset Distillation via Cross-Group Barycenter Alignment
Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei +2
Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit dist…