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메트릭 학습 기반 오픈세트 극소수 학습에 대한 분류 성능 향상
박근철(Keunchul Park),정민기(Minki Jeong),김창익(Changick Kim) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.11
To conduct image classification with limited data is a key challenge for image recognition. Although several few-shot learning models already show good performance in closed-set classification, the actual environment for which deep learning is applied is open-set classification rather than closed-set classification. To achieve good open-set classification performance, we apply meta-learning in our model and suggest a layer normalization technique to make class decision boundaries more accurate. Our method shows better classification accuracy and unknown class sample detection capability, compared with previous few-shot learning methods.