Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On Error Propagation of Diffusion Models
Authors: Yangming Li, Mihaela van der Schaar
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted extensive experiments on multiple image datasets, showing that our proposed regularization reduces error propagation, significantly improves vanilla DMs, and outperforms previous baselines. |
| Researcher Affiliation | Academia | Yangming Li, Mihaela van der Schaar Department of Applied Mathematics and Theoretical Physics University of Cambridge EMAIL |
| Pseudocode | Yes | Algorithm 1: Optimization with Our Proposed Regularization |
| Open Source Code | Yes | The source code of this work is publicly available at a personal repository: https://github.com/louisli321/epdm, and our lab repository: https://github.com/vanderschaarlab/epdm. |
| Open Datasets | Yes | We train standard diffusion models (Ho et al., 2020) on two datasets: CIFAR-10 (32ˆ32) (Krizhevsky et al., 2009) and Image Net (32ˆ32) (Deng et al., 2009). We conduct experiments on three image datasets: CIFAR-10 (Krizhevsky et al., 2009), Image Net (Deng et al., 2009), and Celeb A (Liu et al., 2015), with image shapes respectively as 32 ˆ 32, 32 ˆ 32, and 64 ˆ 64. |
| Dataset Splits | No | The paper does not explicitly state training, validation, and test dataset splits (e.g., percentages or counts) for reproducibility, nor does it explicitly mention a "validation set" in the context of data partitioning. |
| Hardware Specification | Yes | All our model run on 2 4 Tesla V100 GPUs and are trained within two weeks. |
| Software Dependencies | No | The paper mentions using U-Net as the backbone and other common practices for diffusion models, but it does not specify software dependencies with version numbers (e.g., PyTorch version, CUDA version). |
| Experiment Setup | Yes | The configuration of our model follows common practices, we adopt U-Net (Ronneberger et al., 2015) as the backbone and respectively set hyper-parameters T, σt, L, λreg, λnll, ρ as 1000, βt, 5, 0.2, 0.8, 0.003. |