<ul><li>Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data</li><li>A new approach called Meta-Clip is introduced for enhancing the utility of privacy-preserving few-shot learning methods.</li><li>The Adaptive Clipping method dynamically adjusts clipping thresholds during training to balance data privacy preservation with learning capacity maximization.</li><li>Experiments demonstrate the effectiveness of Adaptive Clipping in minimizing utility degradation and improving generalization performance.</li></ul>

Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data

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