이정호 유주한 Kim, Dong Hwan 2024-01-12T02:47:20Z 2024-01-12T02:47:20Z 2023-06-15 2023-02-17 https://pubs.kist.re.kr/handle/201004/76493 https://kros.org/Conference/ConferenceView.asp?AC=0&CODE=CC20220802&B_CATE=BBC1 Currently active learning is used to reduce the training data required for deep learning. In active learning, a few data are sampled from the training dataset to learn a model where the goal of sampling strategy is the sampled data to cover the distribution of the training dataset as much as possible. We propose a method for evaluating sampling strategies of the active learning in the instance segmentation task. We evaluate the sampling strategies by comparing the sampling data and its ground truth(G.T.) data with Mask IoU and Boundary IoU. It was confirmed that both evaluation methods were drawn as upward-sloping graphs as they passed through the sampling stage. Korean 한국로봇학회 IoU 를 이용한 능동 학습의 샘플링 전략 평가방법 Conference 2 제18회 한국로봇종합학술대회 (KRoC 2023) 제18회 한국로봇종합학술대회 (KRoC 2023) KO 휘닉스 평창 2023-02-15 제18회 한국로봇종합학술대회논문집