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DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
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Project page for DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
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DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning Wenxuan Bao 1 , Francesco Pittaluga 2 , Vijay Kumar B G 2 , Vincent Bindschaedler 1 1 University of Florida 2 NEC Labs America NeurIPS 2023 Paper Code arXiv Abstract Data augmentation techniques, such as simple image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approaches, due to the latter's built-in assumption that each training image's contribution to the learned model is bounded. In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data augmentation techniques specifically desi...
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