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researcher

Gunhee Kim

Seoul National University

16 papers hereh-index 436.9k citations102 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author14

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.LG4
  • cs.CL3
affiliations
  • Seoul National University
  • rippleAI
Homepage
same name
  • Gunhee Kim — 21 papers, h 22
  • Gunhee Kim — 11 papers, h 7
  • Gunhee Kim — 9 papers, h 3
  • Gunhee Kim — 8 papers, h 4
  • Gunhee Kim — 6 papers, h 3
  • Gunhee Kim — 6 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedA Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

94 citations · 138 across the 8 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 12 cited

Variational Laplace Autoencoders

Yookoon Park, Chris Dongjoo Kim, Gunhee Kim

Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. However, such amortized variational inference faces two challenges: (…

cs.LG2020★ 94 cited

A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

Soochan Lee, Junsoo Ha, Dongsu Zhang +1

Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and tas…

cs.LG2019★ 10 cited

Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation

Soochan Lee, Junsoo Ha, Gunhee Kim

Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, whic…

cs.LG2018

Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks

Youngjin Kim, Minjung Kim, Gunhee Kim

We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.