Editorial: Privacy-Preserving Deep Heterogeneous View Perception for Data Learning

Peng Li
2022 Frontiers in Neurorobotics  
Deep learning has promoted the development of cutting-edge robotic systems with the ability to automatically mine concepts from complex tasks in an open-ended manner. Many novel algorithms and efficient architectures of deep learning with trainable components have achieved remarkable performance in various domains such as machine learning and robotic devices, based on the unsupervised/supervised learning schemes. Most of the current deep learning methods focus on a single-view perception of
more » ... cts without fully considering the intrinsic characteristics of data, through which objects can be described by heterogeneous views. Those heterogeneous views contain complementary knowledge and information that can further improve representation learning of data. With development for easier access to heterogeneous view data promoted by wider deployments of edge-computing robotic devices, deep heterogeneous view perception distilling knowledge from various views is increasingly attracting more attention. At the same time, heterogeneous view data contains more private information than single view data. Mining large-scale heterogeneous view data inevitably raises the issue of privacy. With the emergence of deep heterogeneous view perception, privacies hidden in data are becoming more easily leaked. Thus, perception of deep heterogeneous view knowledge of data with preserving privacies is also becoming central to neural computing. This Research Topic collects 8 high-quality articles reporting the latest applications of privacy-preserving deep heterogeneous learning. Below is a review of the articles published in this collection.
doi:10.3389/fnbot.2022.862535 pmid:35370595 pmcid:PMC8973699 fatcat:tixdpi47bjf7fbbxbwt4houxsq