Adversarial domain adaptation has made tremendous success by learning domain-invariant feature representations.
As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications.
Avatar refers to a representative of a physical user in the virtual world that can engage in different activities and interact with other objects in metaverse.
WiFi sensing has been evolving rapidly in recent years.
We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement while overcoming the cross-site issue.
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access.
The results validate that our method works well on wireless human activity recognition and person identification systems.
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
We experimentally show that this makes it possible to detect cracks from an image of one-third the resolution of images used for annotation with about the same accuracy.
The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures.
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.