6 papers
Correcting Guided Diffusion Trajectories with Spectral Alignment
Gihoon Kim, Taesup Kim
The practical success of conditional image generation hinges on fine-grained differences in condition alignment and visual fidelity. Classifier-free guidance (CFG) is central to th…
Interweaving Marginals into Multivariate Sample Paths: Training-Free Dependence Construction for Probabilistic Time Series Foundation Models
Jinmyeong Choi, Jinkwan Jang, Seul Lee +1
Probabilistic time series foundation models (TSFMs) provide coordinate-wise predictive distributions, but these marginals do not determine a joint distribution over multivariate fu…
Anchor to Expand: Semantic Anchoring for Personalized Text-to-Image Diffusion Models
Seoyun Yang, Gihoon Kim, Taesup Kim
Personalizing text-to-image diffusion models extends pretrained models to represent novel user-specific concepts from only a few reference images. However, learning a new concept w…
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…
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.…
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…