2 citations · 7 across the 6 of their papers we have counts for
6 papers
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Wasserstein Gradient Flow (WGF) describes the gradient dynamics of probability density within the Wasserstein space. WGF provides a promising approach for conducting optimization o…
Analyzing and Improving Optimal-Transport-based Adversarial Networks
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Optimal Transport (OT) problem aims to find a transport plan that bridges two distributions while minimizing a given cost function. OT theory has been widely utilized in generative…
Accelerated materials language processing enabled by GPT
Jaewoong Choi, Byungju Lee
Materials language processing (MLP) is one of the key facilitators of materials science research, as it enables the extraction of structured information from massive materials scie…
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a given cost function. In this regard, OT between tractable prior distri…
Finding the global semantic representation in GAN through Frechet Mean
Jaewoong Choi, Geonho Hwang, Hyunsoo Cho +1
The ideally disentangled latent space in GAN involves the global representation of latent space with semantic attribute coordinates. In other words, considering that this disentang…
Analyzing the Latent Space of GAN through Local Dimension Estimation
Jaewoong Choi, Geonho Hwang, Hyunsoo Cho +1
The impressive success of style-based GANs (StyleGANs) in high-fidelity image synthesis has motivated research to understand the semantic properties of their latent spaces. In this…