no code implementations • 10 Jul 2020 • Yunho Jeon, Yongseok Choi, Jaesun Park, Subin Yi, Dong-Yeon Cho, Jiwon Kim
However, this is likely to restrict the potential of the target model and some transferred knowledge from the source can interfere with the training procedure.
no code implementations • 8 Jun 2020 • Jin-Hwa Kim, Junyoung Park, Yongseok Choi
To validate our method, we experiment on meta-transfer learning and few-shot learning tasks for multiple settings.
no code implementations • ICLR 2020 • Yongseok Choi, Junyoung Park, Subin Yi, Dong-Yeon Cho
Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain.
no code implementations • 5 Jun 2019 • Junyoung Park, Subin Yi, Yongseok Choi, Dong-Yeon Cho, Jiwon Kim
Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy.
no code implementations • 20 Jun 2018 • Jaehong Kim, Sungeun Hong, Yongseok Choi, Jiwon Kim
Slicing doubly nested network gives a working sub-network.
no code implementations • 11 Jun 2018 • Jaehong Kim, Sangyeul Lee, Sungwan Kim, Moonsu Cha, Jung Kwon Lee, Youngduck Choi, Yongseok Choi, Dong-Yeon Cho, Jiwon Kim
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning.