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DAPPER: Performance Estimation of Domain Adaptation in Mobile Sensing
[article]
2021
arXiv
pre-print
Many applications that utilize sensors in mobile devices and apply machine learning to provide novel services have emerged. However, various factors such as different users, devices, environments, and hyperparameters, affect the performance for such applications, thus making the domain shift (i.e., distribution shift of a target user from the training source dataset) an important problem. Although recent domain adaptation techniques attempt to solve this problem, the complex interplay between
arXiv:2111.11053v1
fatcat:yhgdozpuqbd45bfzqwbuqk6ume