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A Gradient based Adversarial Augmentation Framework for Mitigating Unlearnable Datasets
Unlearnable data methods (ULD) protect public data from unauthorized model training by adding imperceptible perturbations that create a learning shortcut. A deep neural network can exploit the shortcut learning path to minimize training loss without learning generalizable features. Intuitively, find
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