1 citations · 1 across the 4 of their papers we have counts for
4 papers
Backbone Augmented Training for Adaptations
Jae Wan Park, Junhyeok Kim, Youngjun Jun +2
Adaptations facilitate efficient training of large backbone models, including diffusion models for image generation and transformer-based language models. While various adaptation…
PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation Learning
Yeonkyung Lee, Woojung Han, Youngjun Jun +3
Retinal foundation models have significantly advanced retinal image analysis by leveraging self-supervised learning to reduce dependence on labeled data while achieving strong gene…
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models
Youngjun Jun, Jiwoo Park, Kyobin Choo +2
Disentangled representation learning (DRL) aims to break down observed data into core intrinsic factors for a profound understanding of the data. In real-world scenarios, manually…
Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model
Kyobin Choo, Youngjun Jun, Mijin Yun +1
In neuroimaging, generally, brain CT is more cost-effective and accessible imaging option compared to MRI. Nevertheless, CT exhibits inferior soft-tissue contrast and higher noise…