人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
原著論文
Semi-Supervised Learning with Uncertainty
Kazuki YoshiyamaAkito Sakurai
著者情報
ジャーナル フリー

2018 年 33 巻 4 号 p. C-HA2_1-10

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The goal of semi-supervised learning is to utilize many unlabeled samples under a situation where a few labeled samples exist. Recently, researches of semi-supervised learning are evolving with deep learning technology development, because, in deep, models have powerful representation to make use of abundant unlabeled samples. In this paper, we propose a novel semi-supervised learning method with uncertainty. It naturally extends the consistency loss under the uncertainty and propose suitable regularizations for the uncertainty. Using two datasets CIFAR-10 and SVHN and with various experiments, we empirically demonstrate that the proposed method achieves competitive or higher performance in accuracy when compared to semi-supervised learning with the conventional consistency loss while our proposal can let a model generalize much faster.

著者関連情報
© The Japanese Society for Artificial Intelligence 2018
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