614 citations · 1.9k across the 38 of their papers we have counts for
6 papers · 2 filters
Towards Oracle Knowledge Distillation with Neural Architecture Search
Minsoo Kang, Jonghwan Mun, Bohyung Han
We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. Our approach addresses the…
Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling
Heung-Chang Lee, Do-Guk Kim, Bohyung Han
We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search processes. Our approach samples candidate models fro…
Regularizing Neural Networks via Stochastic Branch Layers
Wonpyo Park, Paul Hongsuck Seo, Bohyung Han +1
We introduce a novel stochastic regularization technique for deep neural networks, which decomposes a layer into multiple branches with different parameters and merges stochastical…
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…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Woong-Gi Chang, Tackgeun You, Seonguk Seo +2
We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing ba…