Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟰𝟲 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Mixed Differential Privacy in Computer Vision by amazon Follow me for a similar post: Ashish Patel  ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published arxiv2022. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 ➡️ We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. ➡️ While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. ➡️ A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. ➡️ AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. ➡️ AdaMix reduces the error increase from the non-private upper bound from the 167-311\% of the baseline, on average across 6 datasets, to 68-92\% depending on the desired privacy level selected by the user. ➡️ AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. ➡️ In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis. #computervision #artificialintelligence #technology