Mixup|based Data Augmentation for Differentially Private Learning

Mixup-based Data Augmentation for Differentially Private Learning

Title:DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning ... Abstract:Data augmentation techniques, such as simple image ...

Mixup-based Data Augmentation for Differentially Private Learning

... training data in classical (non-private) learning is data augmentation. Unfortunately, the analysis of differentially private learning mechanisms requires that.

DP-mix: mixup-based data augmentation for differentially private ...

However, such techniques are fundamentally incompatible with differentially private learning approaches, due to the latter's built-in assumption ...

DP-Mix: Mixup-based Data Augmentation for Differentially Private ...

Differentially private (DP) machine learning techniques are notorious for their degradation of model utility (e.g., they degrade classification ...

DP-Mix: Mixup-based Data Augmentation for Differentially Private ...

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Mixup-based Data Augmentation for Differentially Private Learning

DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning. Wenxuan Bao, Francesco Pittaluga, Vijay Kumar B G, Vincent Bindschaedler. Nov 02 ...

wenxuan-Bao/DP-Mix: Code for "DP-Mix - GitHub

This is the code to reproduce the methods proposed in DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning.

Differential Privacy and Mixup

Randomness can even strengthen the training performance, e.g., random dropout [SHK+14] and data augmentation [SK19]. Generally speaking, data aug- mentation ...

‪Wenxuan Bao‬ - ‪Google Scholar‬

DP-mix: mixup-based data augmentation for differentially private learning. W Bao, F Pittaluga, VK BG, V Bindschaedler. Advances in Neural Information Processing ...

Data Augmentation | vbinds.ch - Vincent Bindschaedler

DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning. Type. Conference. In. NeurIPS. Year. 2023. By. Wenxuan Bao · Francesco Pittaluga.

Differentially Private Optimization Improvement through Mixup

Randomness can even strengthen the training performance such as random dropout [16] and data augmentation [17]. Generally speaking, data augmentation represents ...

Unlocking High-Accuracy Differentially Private Image Classification ...

Unless otherwise specified, we train without data augmentation. For all experiments in this subsection, we tune the learning rate 𝜂 and the noise parameter 𝜎 on ...

Differentially Private CutMix for Split Learning with Vision Transformer

DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning · Wenxuan BaoFrancesco PittalugaVijay KumarVincent Bindschaedler. Computer Science.

Differentially Private Diffusion Models - OpenReview

usion models could be beneficial, and then proposes a DP-SGD based training method for di! ... data augmentation technique for enriching training data. We would ...

Federated Partial Label Learning with Local-Adaptive Augmentation ...

The MixUp-based local-adaptive data augmentation. The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24). 16272. Page 2. is designed to ...

A Neural Database for Differentially Private Spatial Range Queries

Thus, in the second stage, we synthesize more training sam- ples based on the collected data to boost learning accuracy, in a step called data augmentation.

[PDF] XOR Mixup: Privacy-Preserving Data Augmentation for One ...

Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise · Yusuke KodaKoji YamamotoT. NishioM. Morikura. Computer ...

Differentially Private Federated Learning on Heterogeneous Data

We present DP-SCAFFOLD, a novel dif- ferential private FL algorithm for training a global model from heterogeneous data based on SCAFFOLD. (Karimireddy et al., ...

Mixup data augmentation - Page 2 - fastai dev - fast.ai Course Forums

Mixup is a tool to avoid ovefitting, so yes, it's normal your training loss is bigger. Try reducing the alpha parameter.

Differential Privacy | vbinds.ch - Vincent Bindschaedler

DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning. Type. Conference. In. NeurIPS. Year. 2023. By. Wenxuan Bao · Francesco Pittaluga.