726 citations · 826 across the 5 of their papers we have counts for
5 papers
Learning Student-Friendly Teacher Networks for Knowledge Distillation
Dae Young Park, Moon-Hyun Cha, Changwook Jeong +2
We propose a novel knowledge distillation approach to facilitate the transfer of dark knowledge from a teacher to a student. Contrary to most of the existing methods that rely on e…
Stochastic Doubly Robust Gradient
Kanghoon Lee, Jihye Choi, Moonsu Cha +2
When training a machine learning model with observational data, it is often encountered that some values are systemically missing. Learning from the incomplete data in which the mi…
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…
Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks
Taeksoo Kim, Byoungjip Kim, Moonsu Cha +1
Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfe…
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim +2
While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and need…