2 citations · 3 across the 5 of their papers we have counts for
5 papers
Distilling Dataset into Neural Field
Donghyeok Shin, HeeSun Bae, Gyuwon Sim +2
Utilizing a large-scale dataset is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these…
Unknown Domain Inconsistency Minimization for Domain Generalization
Seungjae Shin, HeeSun Bae, Byeonghu Na +2
The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific…
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
HeeSun Bae, Seungjae Shin, Byeonghu Na +1
For learning with noisy labels, the transition matrix, which explicitly models the relation between noisy label distribution and clean label distribution, has been utilized to achi…
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin +3
Recent Vision-Language Pretrained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a meth…
Frequency Domain-based Dataset Distillation
Donghyeok Shin, Seungjae Shin, Il-Chul Moon
This paper presents FreD, a novel parameterization method for dataset distillation, which utilizes the frequency domain to distill a small-sized synthetic dataset from a large-size…