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http://dx.doi.org/10.17703/JCCT.2022.8.1.545

An Analysis of the methods to alleviate the cost of data labeling in Deep learning  

Han, Seokmin (Dept. of Data science, Korea National University of Transportation)
Publication Information
The Journal of the Convergence on Culture Technology / v.8, no.1, 2022 , pp. 545-550 More about this Journal
Abstract
In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. And it also requires the labeling of each data to fully train the neural network, which means that experts should spend lots of time to provide the labeling. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, those methods are analyzed and its possible future direction of the research is suggested.
Keywords
Deep Learning; Labeling; Uncertainty; Similarity;
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Times Cited By KSCI : 3  (Citation Analysis)
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