16 citations · 105 across the 22 of their papers we have counts for
25 papers
Loss-Curvature Matching for Dataset Selection and Condensation
Seungjae Shin, Heesun Bae, Donghyeok Shin +2
Training neural networks on a large dataset requires substantial computational costs. Dataset reduction selects or synthesizes data instances based on the large dataset, while mini…
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…
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
JoonHo Jang, Byeonghu Na, DongHyeok Shin +3
Open-Set Domain Adaptation (OSDA) assumes that a target domain contains unknown classes, which are not discovered in a source domain. Existing domain adversarial learning methods a…
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
HeeSun Bae, Seungjae Shin, Byeonghu Na +3
Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing…
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…
Automatic Calibration Framework of Agent-Based Models for Dynamic and Heterogeneous Parameters
Dongjun Kim, Tae-Sub Yun, Il-Chul Moon +1
Agent-based models (ABMs) highlight the importance of simulation validation, such as qualitative face validation and quantitative empirical validation. In particular, we focused on…