25 citations · 39 across the 6 of their papers we have counts for
6 papers
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization
Yunho Jeon, Yongseok Choi, Jaesun Park +3
Training a deep neural network with a small amount of data is a challenging problem as it is vulnerable to overfitting. However, one of the practical difficulties that we often fac…
Multi-step Estimation for Gradient-based Meta-learning
Jin-Hwa Kim, Junyoung Park, Yongseok Choi
Gradient-based meta-learning approaches have been successful in few-shot learning, transfer learning, and a wide range of other domains. Despite its efficacy and simplicity, the bu…
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators
Yongseok Choi, Junyoung Park, Subin Yi +1
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-traini…
Discriminative Few-Shot Learning Based on Directional Statistics
Junyoung Park, Subin Yi, Yongseok Choi +2
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. We propose a novel algorit…
Doubly Nested Network for Resource-Efficient Inference
Jaehong Kim, Sungeun Hong, Yongseok Choi +1
We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While…
Auto-Meta: Automated Gradient Based Meta Learner Search
Jaehong Kim, Sangyeul Lee, Sungwan Kim +6
Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and tim…