Publications
![[P5] MaDeL : Manifold-Decomposed Feature Losses for Generative Modeling](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/MaDeL.png)
Preprint
[P5] MaDeL : Manifold-Decomposed Feature Losses for Generative Modeling
Beomsu Kim, Jong Chul Ye†, Kwanyoung Kim†† Co-Corresponding Author
Preprint2026
![[P3] VGAS: Variance-Reduced Guidance and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/vgas.png)
Preprint
[P3] VGAS: Variance-Reduced Guidance and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion
Kwanyoung Kim
Preprint2026
![[P2] Directional Attention Guidance for Diffusion Models via Sparse Hopfield Retrieval](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/DAG.png)
Preprint
[P2] Directional Attention Guidance for Diffusion Models via Sparse Hopfield Retrieval
Kwanyoung Kim
Preprint2026
![[P1] UNICORN: Ultrasound Nakagami Imaging with Score Matching and Adaptation for Assessing Hepatic Steatosis](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/unicorn.png)
Preprint
[P1] UNICORN: Ultrasound Nakagami Imaging with Score Matching and Adaptation for Assessing Hepatic Steatosis
Kwanyoung Kim*, Jaa-yeon Lee*, Youngjun Ko, GunWoo Lee, Jong Chul Ye* Equal Contribution
Preprint2026
![[C15] Adjoint Guidance Flow for Efficient Critic-Guided Vision-Language-Action Models](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/AGF.png)
Workshop
[C15] Adjoint Guidance Flow for Efficient Critic-Guided Vision-Language-Action Models
Jeongsol Kim, Kwanyoung Kim†, Jong Chul Ye†† Co-Corresponding Author
NeurIPS 2026 Workshop (WRL)2026
![[C14] GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/vgas.png)
OralWorkshop
[C14] GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion
Kwanyoung Kim
NeurIPS 2026 Workshop (BeNTo), Oral2026
![[C13] Reward Sharpness-Aware Fine-Tuning for Diffusion Models](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/rsa-ft_concept3.png)
Top-tier
[C13] Reward Sharpness-Aware Fine-Tuning for Diffusion Models
Kwanyoung Kim*, Byeoung Su Sim** Equal Contribution
CVPR 20262026
![[C11] Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/ASAG.jpg)
Top-tier
[C11] Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance
Kwanyoung Kim
AAAI 20262026
![[J2] End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/ceft.png)
Impact Factor: 11.8
[J2] End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
Kwanyoung Kim*, Yujin Oh*, Sangjoon Park*, Hwa Kyung Byun, Jin Sung Kim, Yong Bae Kim, Jong-Chul Ye* Equal Contribution
Medical Image Analysis (MedIA)2025
![[J1] Unsupervised Training of Denoisers for Low-Dose CT Reconstruction Without Full-Dose Ground Truth](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/JSTSP.png)
Impact Factor: 6.7
[J1] Unsupervised Training of Denoisers for Low-Dose CT Reconstruction Without Full-Dose Ground Truth
Kwanyoung Kim, Shakarim Soltanayev, Se Young Chun
IEEE Journal of Selected Topics in Signal Processing2021
![[C3] NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/video2.png)
Workshop
[C3] NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results
Seungjun Nah, Radu Timofte, Including (Kwanyoung Kim)
CVPRW 20192019
![[C2] Efficient Module Based Single Image Super Resolution for Multiple Problems](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/embsr2.png)
Workshop
[C2] Efficient Module Based Single Image Super Resolution for Multiple Problems
Dongwon Park*, Kwanyoung Kim*, Se Young Chun* Equal Contribution
CVPRW 20182018
![[C1] NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/embsr.png)
Workshop
[C1] NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Radu Timofte, Including (Kwanyoung Kim)
CVPRW 20182018
![[P7] VaLoP: Value Gradient Lookahead and Pullback for Reward-Guided Diffusion](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/Valop_teaser.png)
![[P6] GenTri: Generative Triangulation for Occlusion-Robust 3D Mouse Pose Estimation](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/GenTri.png)
![[C16] Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/ibcd_page-0001.jpg)
![[C12] Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/BANSA.jpg)
![[C10] PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/PLADIS.jpg)
![[C9] OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/OTseg.png)
![[C8] Unpaired Image-to-Image Translation via Neural Schrödinger Bridge](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/UNSB.png)
![[C7] Noise2One: One-Shot Image Denoising with Local Implicit Learning](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/Noise2One.jpg)
![[C6] Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/Noise2AdapScore.png)
![[C5] Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/Noise2Score.png)
![[C4] Task-Aware Variational Adversarial Active Learning](https://raw.githubusercontent.com/cubeyoung/k_0.kim.github.io/refs/heads/master/source/images/TAVAAL.png)