83 citations · 756 across the 59 of their papers we have counts for
60 papers
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation
Boah Kim, Yujin Oh, Bradford J. Wood +2
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in i…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance
Gihyun Kwon, Jong Chul Ye
Diffusion models have shown significant progress in image translation tasks recently. However, due to their stochastic nature, there's often a trade-off between style transformatio…
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Geon Yeong Park, Jeongsol Kim, Beomsu Kim +2
Despite the remarkable performance of text-to-image diffusion models in image generation tasks, recent studies have raised the issue that generated images sometimes cannot capture…
Data Topology-Dependent Upper Bounds of Neural Network Widths
Sangmin Lee, Jong Chul Ye
This paper investigates the relationship between the universal approximation property of deep neural networks and topological characteristics of datasets. Our primary contribution…
Direct Diffusion Bridge using Data Consistency for Inverse Problems
Hyungjin Chung, Jeongsol Kim, Jong Chul Ye
Diffusion model-based inverse problem solvers have shown impressive performance, but are limited in speed, mostly as they require reverse diffusion sampling starting from noise. Se…
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Beomsu Kim, Gihyun Kwon, Kwanyoung Kim +1
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved…
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
Suhyeon Lee, Won Jun Kim, Jinho Chang +1
Following the impressive development of LLMs, vision-language alignment in LLMs is actively being researched to enable multimodal reasoning and visual IO. This direction of researc…
Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion
Inhwa Han, Serin Yang, Taesung Kwon +1
Diffusion models have shown superior performance in image generation and manipulation, but the inherent stochasticity presents challenges in preserving and manipulating image conte…
Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer
Serin Yang, Hyunmin Hwang, Jong Chul Ye
Diffusion models have shown great promise in text-guided image style transfer, but there is a trade-off between style transformation and content preservation due to their stochasti…
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
Hyungjin Chung, Suhyeon Lee, Jong Chul Ye
Krylov subspace, which is generated by multiplying a given vector by the matrix of a linear transformation and its successive powers, has been extensively studied in classical opti…
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model
Jaeyoung Huh, Sangjoon Park, Jeong Eun Lee +1
Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications o…
Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models
Hyeonho Jeong, Gihyun Kwon, Jong Chul Ye
Recent advancements in large scale text-to-image models have opened new possibilities for guiding the creation of images through human-devised natural language. However, while prio…
Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction
Gyutaek Oh, Jeong Eun Lee, Jong Chul Ye
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep le…
Don't Play Favorites: Minority Guidance for Diffusion Models
Soobin Um, Suhyeon Lee, Jong Chul Ye
We explore the problem of generating minority samples using diffusion models. The minority samples are instances that lie on low-density regions of a data manifold. Generating a su…
ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts
Kwanyoung Kim, Yujin Oh, Jong Chul Ye
Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) has led to great promise in zero-shot semantic segmentation by transferring image-text aligned knowledg…
Minimizing Trajectory Curvature of ODE-based Generative Models
Sangyun Lee, Beomsu Kim, Jong Chul Ye
Recent ODE/SDE-based generative models, such as diffusion models, rectified flows, and flow matching, define a generative process as a time reversal of a fixed forward process. Eve…
Single-round Self-supervised Distributed Learning using Vision Transformer
Sangjoon Park, Ik-Jae Lee, Jun Won Kim +1
Despite the recent success of deep learning in the field of medicine, the issue of data scarcity is exacerbated by concerns about privacy and data ownership. Distributed learning a…
Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model
Jinho Chang, Jong Chul Ye
The recent success of large foundation models in artificial intelligence has prompted the emergence of chemical pre-trained models. Despite the growing interest in large molecular…
Magnitude and Angle Dynamics in Training Single ReLU Neurons
Sangmin Lee, Byeongsu Sim, Jong Chul Ye
To understand learning the dynamics of deep ReLU networks, we investigate the dynamic system of gradient flow by decomposing it to magnitude and angle $ϕ(t):= π- θ(t)…
Self-supervised debiasing using low rank regularization
Geon Yeong Park, Chanyong Jung, Sangmin Lee +2
Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. While most existing debiasing methods require full supervision on either sp…
Training Debiased Subnetworks with Contrastive Weight Pruning
Geon Yeong Park, Sangmin Lee, Sang Wan Lee +1
Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: ``Doe…
Pyramidal Denoising Diffusion Probabilistic Models
Dohoon Ryu, Jong Chul Ye
Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training an…
Denoising MCMC for Accelerating Diffusion-Based Generative Models
Beomsu Kim, Jong Chul Ye
Diffusion models are powerful generative models that simulate the reverse of diffusion processes using score functions to synthesize data from noise. The sampling process of diffus…