Meta Dropout: Learning to Perturb Latent Features for Generalization

ICLR 2020 Meta Dropout: Learning to Perturb Latent Features for Generalization Hae Beom Lee, Taewook Nam, Eunho Yang, Sung Ju Hwang  A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb tra...

Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks

ICLR 2020 Learning to Balance: Bayesian Meta-Learning forImbalanced and Out-of-distribution Tasks Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang  While tasks could come with varying the number of instances and classes in realistic sett...

Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

ICLR 2019 Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningYanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a ...

InstaGAN: Instance-aware Image-to-Image Translation

ICLR 2019 InstaGAN: Instance-aware Image-to-ImageTranslation Sangwoo Mo, Minsu Cho, Jinwoo Shin  Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous meth...

Lifelong Learning with Dynamically Expandable Networks

ICLR 2018 Lifelong Learning with Dynamically Expandable NetworksJaehong Yoon, Eunho Yang, Jeongtae Lee, Sungju Hwang We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacit...