This paper considers the problem of differentially private semi-supervised transfer and multi-task learning. The notion of \emph{membership-mapping} has been developed using measure theory basis to learn data representation via a fuzzy membership function. An alternative conception of deep autoencoder, referred to as \emph{Conditionally Deep Membership-Mapping Autoencoder (CDMMA)}, is considered for transferrable deep learning. Under practice-oriented settings, an analytical solution for the learning of CDMMA can be derived by means of variational optimization. The paper proposes a transfer and multi-task learning approach that combines CDMMA with a tailored noise adding mechanism to achieve a given level of privacy-loss bound with the minimum perturbation of the data. Numerous experiments were carried out using MNIST, USPS, Office, and Caltech256 datasets to verify the competitive robust performance of the proposed methodology.

翻译：本文探讨了不同私人半监督转让和多任务学习的问题。 \ emph{ 会员- mapping} 的概念是利用计量理论基础开发的,通过模糊的会籍功能学习数据代表。 深自动编码器的另一种概念,称为 emph{ 有条件的深入会籍映射自动编码器(CDMA),是考虑可转移的深层次学习的。 在面向实践的设置下,通过变式优化,可以产生一种学习CDMMA的分析性解决办法。 该文件提出一种转让和多任务学习方法,将CDMMA与一个定制的添加噪音的机制结合起来,以实现一定程度的隐私损失,同时尽量减少数据的扰动。 许多实验都是利用MMIST、USPS、Office和Caltech256数据集进行的,以核实拟议方法的竞争性强健表现。