Publications
금융권 유일의 연구 조직으로 다양한 신기술 영역에서 하나금융그룹의 위상을 높이고
세계적 권위의 학회에서 대외 성과를 달성하고 있습니다.
Papers
Auxiliary Class Based Multiple Choice Learning
- 발행일
- 2021.08.06
- 발행기관
- arXiv(2021)
- 저자
- Sihwan Kim, Dae Yon Jung, Taejang Park
- Link
- https://arxiv.org/abs/2108.02949
초록
The merit of ensemble learning lies in having different outputs from many individual models on a single input, i.e., the diversity of the base models. The high quality of diversity can be achieved when each model is specialized to different subsets of the whole dataset. Moreover, when each model explicitly knows to which subsets it is specialized, more opportunities arise to improve diversity. In this paper, we propose an advanced ensemble method, called Auxiliary class based Multiple Choice Learning (AMCL), to ultimately specialize each model under the framework of multiple choice learning (MCL). The advancement of AMCL is originated from three novel techniques which control the framework from different directions: 1) the concept of auxiliary class to provide more distinct information through the labels, 2) the strategy, named memory-based assignment, to determine the association between the inputs and the models, and 3) the feature fusion module to achieve generalized features. To demonstrate the performance of our method compared to all variants of MCL methods, we conduct extensive experiments on the image classification and segmentation tasks. Overall, the performance of AMCL exceeds all others in most of the public datasets trained with various networks as members of the ensembles.
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