2024/05/28 by Byeonghu Na, Na, Byeonghu, Yeongmin Kim +9 · 3 citations
Engineering · Environmental Science · Medicine · #Asphalt Pavement Performance Evaluation #FOS: Computer and information sciences #Groundwater flow and contamination studies #Machine Learning (cs.LG) #Mycobacterium research and diagnosis
paper · pdf · doi:10.48550/arxiv.2405.17880
openalex publication_date 2024/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent advances in powerful pre-trained diffusion models encourage the development of methods to improve the sampling performance under well-trained diffusion models. This paper introduces Diffusion Rejection Sampling (DiffRS), which uses a rejection sampling scheme that aligns the sampling transition kernels with the true ones at each timestep. The proposed method can be viewed as a mechanism that evaluates the quality of samples at each intermediate timestep and refines them with varying effort depending on the sample. Theoretical analysis shows that DiffRS can achieve a tighter bound on sampling error compared to pre-trained models. Empirical results demonstrate the state-of-the-art performance of DiffRS on the benchmark datasets and the effectiveness of DiffRS for fast diffusion samplers and large-scale text-to-image diffusion models. Our code is available at https://github.com/aailabkaist/DiffRS.