7 papers
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
Xinyue Ai, Yutong He, Albert Gu +4
Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet par…
Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation
Xintong Duan, Yutong He, Fahim Tajwar +3
Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation. While consistency models offer a potential sol…
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Yudi Sang, Yanzhen Liu, Sutuke Yibulayimu +33
The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and effici…
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models
Xintong Duan, Yutong He, Fahim Tajwar +3
Many real-world decision-making problems are combinatorial in nature, where states (e.g., surrounding traffic of a self-driving car) can be seen as a combination of basic elements…
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
Michail Dontas, Yutong He, Naoki Murata +3
This paper considers blind inverse image restoration, the task of predicting a target image from a degraded source when the degradation (i.e. the forward operator) is unknown. Exis…