13 citations · 13 across the 1 of their papers we have counts for
3 papers
cs.CV2019★ 13 cited
Fine-Grained Neural Architecture Search
Heewon Kim, Seokil Hong, Bohyung Han +2
We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even gene…
cs.LG2019
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Dongmin Park, Seokil Hong, Bohyung Han +1
Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suf…
cs.LG2019
Learning to Forget for Meta-Learning
Sungyong Baik, Seokil Hong, Kyoung Mu Lee
Few-shot learning is a challenging problem where the goal is to achieve generalization from only few examples. Model-agnostic meta-learning (MAML) tackles the problem by formulatin…