Email this article Privileged Learning Using Regularization in the Problem of Evaluating the Human Posture
- Authors: Kaprielova M.S.1, Neichev R.G.2, Tikhonova A.D.2
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Affiliations:
- Federal Research Center “Computer Science and Control,” Russian Academy of Sciences, 119333, Moscow, Russia
- Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow oblast, Russia
- Issue: No 4 (2023)
- Pages: 121-124
- Section: ARTIFICIAL INTELLIGENCE
- URL: https://journals.rcsi.science/0002-3388/article/view/136883
- DOI: https://doi.org/10.31857/S000233882303006X
- EDN: https://elibrary.ru/EULHZW
- ID: 136883
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Abstract
The problem of evaluating a person’s posture from video data is solved. Various key points of the human body are analyzed. We study the change in the accuracy of a fixed model when using different proportions in the regularization term of the loss function. It is shown that for a fixed number of training epochs, the accuracy of the model differs depending on the selected proportions. In addition, it is shown that the linear correlation between the trajectories of the key points that are part of the regularization term is not the main criterion for predicting the effectiveness of applying the regularization term of the loss function.
About the authors
M. S. Kaprielova
Federal Research Center “Computer Science and Control,” Russian Academy of Sciences, 119333, Moscow, Russia
Email: kaprielova.ms@phystech.edu
Россия, Москва
R. G. Neichev
Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow oblast, Russia
Email: neychev@phystech.edu
Россия, Москва
A. D. Tikhonova
Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow oblast, Russia
Author for correspondence.
Email: tikhonova.ad@phystech.edu
Россия, Москва
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