Jia Ni
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126
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2026
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
This paper systematically investigates unlearnable examples (UEs) across diverse training paradigms, finding that existing UEs fail under pretraining-finetuning (PF) settings, and proposes Shallow Semantic Camouflage (SSC) to maintain unlearnability.
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cs.LGcs.AIcs.CRRecentApr 18, 2026
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
Bo Wang, Jia Ni, Mengnan Zhao, Zhan Qin +1 more
This paper systematically investigates unlearnable examples (UEs) across diverse training paradigms, finding that existing UEs fail under pretraining-finetuning (PF) settings, and proposes Shallow Sem…
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