A Note on the Convergence of Denoising Diffusion Probabilistic Models
Authors: Sokhna Diarra Mbacke, Omar Rivasplata
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The goal of these experiments is to assess the numerical value of the bound of Theorem 3.1 on a synthetic dataset. The data-generating distribution is chosen to be the uniform distribution on the square of side 2, centered at the origin. Figure 2 shows samples from this target distribution. The backward process uses a shared network with fully connected layers, and 128 hidden units each. The model is trained on 50, 000 samples from the original distribution, and the bound is computed with n = 5, 000 independent samples. Samples from the trained model are shown in Figure 3. We computed the bound for different values of λ. |
| Researcher Affiliation | Academia | Sokhna Diarra Mbacke EMAIL Université Laval Omar Rivasplata EMAIL University College London |
| Pseudocode | No | The paper describes mathematical derivations and theoretical results. It does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The data-generating distribution is chosen to be the uniform distribution on the square of side 2, centered at the origin. Figure 2 shows samples from this target distribution. This is a synthetic dataset generated for the purpose of the paper, but no specific access information (link, citation, or generation script) is provided to reproduce this exact dataset. |
| Dataset Splits | No | The model is trained on 50,000 samples from the original distribution, and the bound is computed with n = 5,000 independent samples. While sample sizes are mentioned for training and bound computation, there is no explicit description of traditional training/validation/test splits for the model's evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names along with their version numbers. |
| Experiment Setup | Yes | The backward process uses a shared network with fully connected layers, and 128 hidden units each. The model is trained on 50, 000 samples from the original distribution, and the bound is computed with n = 5, 000 independent samples. We estimated the Lipschitz norms Kt θ using K t from Remark 3.2, and the expected norms in the last two terms of Theorem 3.1 are estimated using 106 independent samples from each distribution. |