100 citations · 146 across the 9 of their papers we have counts for
9 papers
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
Dongjun Kim, Yeongmin Kim, Se Jung Kwon +2
The proposed method, Discriminator Guidance, aims to improve sample generation of pre-trained diffusion models. The approach introduces a discriminator that gives explicit supervis…
Maximum Likelihood Training of Implicit Nonlinear Diffusion Models
Dongjun Kim, Byeonghu Na, Se Jung Kwon +3
Whereas diverse variations of diffusion models exist, extending the linear diffusion into a nonlinear diffusion process is investigated by very few works. The nonlinearity effect h…
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song +2
Recent advances in diffusion models bring state-of-the-art performance on image generation tasks. However, empirical results from previous research in diffusion models imply an inv…
Neural Posterior Regularization for Likelihood-Free Inference
Dongjun Kim, Kyungwoo Song, Seungjae Shin +3
A simulation is useful when the phenomenon of interest is either expensive to regenerate or irreproducible with the same context. Recently, Bayesian inference on the distribution o…
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
Hyemi Kim, Seungjae Shin, JoonHo Jang +4
The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating th…
Sequential Likelihood-Free Inference with Neural Proposal
Dongjun Kim, Kyungwoo Song, YoonYeong Kim +4
Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulat…