On Error Propagation of Diffusion Models
Authors: Yangming Li, Mihaela van der Schaar
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 yl874@cam.ac.uk |
| 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. |