4 papers
Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift
Gihoon Kim, Hyungjin Park, Taesup Kim
Personalizing text-to-image diffusion models involves integrating novel visual concepts from a small set of reference images while retaining the model's original generative capabil…
Contrastive Residual Energy Test-time Adaptation
Yewon Han, Seoyun Yang, Taesup Kim
Test-time adaptation (TTA) enhances model robustness by enabling adaptation to target distributions that differ from training distributions, improving real-world generalizability.…
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi +1
Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to mi…
Semantic Anchoring for Robust Personalization in Text-to-Image Diffusion Models
Seoyun Yang, Gihoon Kim, Taesup Kim
Text-to-image diffusion models have achieved remarkable progress in generating diverse and realistic images from textual descriptions. However, they still struggle with personaliza…