13 citations · 20 across the 10 of their papers we have counts for
10 papers
A Mechanistic Study of Algorithmic Generalization in Diffusion Models on Modular Arithmetic
Joon Hyeok Kim, Yong-Hyun Park, Mattis Dalsætra Østby +1
Despite their empirical success, how diffusion models generalize remains poorly understood from a mechanistic perspective. In this work, we provide a controlled mechanistic account…
OmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies
Yunzhou Song, Long Le, Yong-Hyun Park +7
Vision-language-action (VLA) models have shown great promise as generalist policies for a large range of relatively simple tasks. However, they demonstrate limited performance on m…
APPLE: Attribute-Preserving Pseudo-Labeling for Diffusion-Based Face Swapping
Jiwon Kang, Yeji Choi, JoungBin Lee +6
Face swapping aims to transfer the identity of a source face onto a target face while preserving target-specific attributes such as pose, expression, lighting, skin tone, and makeu…
DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization
Geonhui Jang, Jin-Hwa Kim, Yong-Hyun Park +3
Text-to-image (T2I) models can effectively capture the content or style of reference images to perform high-quality customization. A representative technique for this is fine-tunin…
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment
Yong-Hyun Park, Junghoon Seo, Bomseok Park +2
Identifying the relevant input features that have a critical influence on the output results is indispensable for the development of explainable artificial intelligence (XAI). Remo…
Direct Unlearning Optimization for Robust and Safe Text-to-Image Models
Yong-Hyun Park, Sangdoo Yun, Jin-Hwa Kim +5
Recent advancements in text-to-image (T2I) models have unlocked a wide range of applications but also present significant risks, particularly in their potential to generate unsafe…
Upsample Guidance: Scale Up Diffusion Models without Training
Juno Hwang, Yong-Hyun Park, Junghyo Jo
Diffusion models have demonstrated superior performance across various generative tasks including images, videos, and audio. However, they encounter difficulties in directly genera…
Resolution Chromatography of Diffusion Models
Juno Hwang, Yong-Hyun Park, Junghyo Jo
Diffusion models generate high-resolution images through iterative stochastic processes. In particular, the denoising method is one of the most popular approaches that predicts the…
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry
Yong-Hyun Park, Mingi Kwon, Jaewoong Choi +2
Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space. To understand the latent space , we analy…
Unsupervised Discovery of Semantic Latent Directions in Diffusion Models
Yong-Hyun Park, Mingi Kwon, Junghyo Jo +1
Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space. While image editing with GANs builds upon latent space, DMs rely on edi…