4 citations · 12 across the 19 of their papers we have counts for
5 papers · 1 filter
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis
Chia-Yi Hsu, Jia-You Chen, Yu-Lin Tsai +4
Differentially private (DP) synthetic data has become the de facto standard for releasing sensitive data. However, many DP generative models suffer from the low utility of syntheti…
Differentially Private Fine-Tuning of Diffusion Models
Yu-Lin Tsai, Yizhe Li, Zekai Chen +4
The integration of Differential Privacy (DP) with diffusion models (DMs) presents a promising yet challenging frontier, particularly due to the substantial memorization capabilitie…
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
Shyam Marjit, Harshit Singh, Nityanand Mathur +3
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitation…
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Chih-Hsun Lin, Chia-Yi Hsu, Chia-Mu Yu +2
Differentially private synthetic data is a promising alternative for sensitive data release. Many differentially private generative models have been proposed in the literature. Unf…
Exploring the Benefits of Visual Prompting in Differential Privacy
Yizhe Li, Yu-Lin Tsai, Xuebin Ren +2
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this w…