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논문 기본 정보

자료유형
학술저널
저자정보
Widhianingsih, Tintrim Dwi Ary (Department of Computer Engineering, Dongseo University) Kang, Dae-Ki (Department of Computer Engineering, Dongseo University)
저널정보
한국인터넷방송통신학회 International journal of advanced smart convergence International journal of advanced smart convergence 제8권 제4호
발행연도
2019.1
수록면
68 - 74 (7page)

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Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

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