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..
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution
Authors: Yutao Yuan, Chun Yuan
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on DIV2K, Image Net, and Celeb A demonstrate that our method achieves higher super-resolution quality than existing diffusion-based image super-resolution methods while having lower time consumption. |
| Researcher Affiliation | Academia | Yutao Yuan, Chun Yuan Tsinghua University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks clearly labeled as such. |
| Open Source Code | Yes | Our code is available at https://github.com/Yuan-Yutao/ECDP. |
| Open Datasets | Yes | For general image super-resolution (4 ), we evaluate the performance of various methods on two datasets, DIV2K (Agustsson and Timofte 2017) and Image Net (Russakovsky et al. 2015). ... For face image super-resolution (8 ), we train and evaluate on Celeb A. |
| Dataset Splits | No | The paper describes data usage for training and evaluation (e.g., 'trained using random HR image crops', 'evaluated with full-size images', 'center cropped and resized'), but does not explicitly provide quantitative train/validation/test dataset splits (e.g., percentages or exact counts) or reference to standard numerical splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and architectures like 'U-Net architecture', 'Big GAN residual blocks', 'VGG network', and 'Runge Kutta ODE solver', but does not provide specific version numbers for any of these or other software dependencies. |
| Experiment Setup | Yes | In our experiments, we set β(t) in our forward process (5) to a linear function increasing from β(0) = 0.1 and β(T) = 20, matching the settings in VP-SDE (Song et al. 2021). ... To generate HR images, we perform probability flow sampling using a standard Runge Kutta ODE solver with absolute and relative error tolerance of 10^-4. |