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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance
Authors: Anh-Dung Dinh, Daochang Liu, Chang Xu
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 23,543 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,435 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results evidently improve over different baselines on datasets with various resolutions. (Abstract) and Section 6. Experiments |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Sydney. Correspondence to: Anh-Dung Dinh <EMAIL>, Chang Xu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DDPM denoising process with guidance and Algorithm 2 Pixel As Params Denoising Process (Px P) |
| Open Source Code | Yes | The code for the proposed sampling process is available here: https://github.com/dungdinhanh/pxpguided-diffusion |
| Open Datasets | Yes | All experiments are conducted on the Image Net dataset at 64x64, 128x128, 256x256, and CIFAR10 to evaluate our method since these datasets offer classification classes and pretrained diffusion models. |
| Dataset Splits | No | The paper uses well-known datasets but does not explicitly provide details about training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All the experiments are run with cluster nodes with 8GPUs NVIDIA A-100. |
| Software Dependencies | No | The paper does not explicitly provide a list of software dependencies with specific version numbers (e.g., Python, PyTorch, or library versions). |
| Experiment Setup | Yes | All the hyperparameters for reproducing all the results are available in Table 7. (Table 7 provides specific values for hyperparameters like δ1, δ2, δ3, δ4, s, and TIME-STEPS for various models and datasets). |