论文提出了深域名适应框架DPDA，结合差分隐私保护敏感训练数据，降低隐私泄露风险。通过在未标记目标数据上使用对抗性学习策略获取领域不变特征，保护医院等机构上传数据的隐私。
目录导引
系列传送
【Active Learning】
【论文笔记01】Learning Loss for Active Learning, CVPR 2019
【论文笔记02】Active Learning For Convolutional Neural Networks: A Core-Set Approch, ICLR 2018
【论文笔记03】Variational Adversarial Active Learning, ICCV 2019
【论文笔记04】Ranked Batch-Mode Active Learning，ICCV 2016
【Transfer Learning】
【论文笔记05】Active Transfer Learning, IEEE T CIRC SYST VID 2020
【论文笔记06】Domain-Adversarial Training of Neural Networks, JMLR 2016
【论文笔记10】A unified framework of active transfer learning for cross-system recommendation, AI 2017
【论文笔记14】Transfer Learning via Minimizing the Performance Gap Between Domains, NIPS 2019
【Differential Privacy】
【论文笔记07】A Survey on Differentially Private Machine Learning, IEEE CIM 2020
【论文笔记09】Differentially Private Hypothesis Transfer Learning, ECML&PKDD 2018
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