726 citations · 1.1k across the 6 of their papers we have counts for
6 papers
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…
Continual Learning with Deep Generative Replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim +1
Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply r…
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…
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification \cite…
Deeply-Recursive Convolutional Network for Image Super-Resolution
Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasin…