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..
Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder
Authors: Yiyang Ma, Wenhan Yang, Jiaying Liu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the superiority of our method in both distortion and perception compared with previous perceptual compression methods. |
| Researcher Affiliation | Academia | 1Wangxuan Institute of Computer Technology, Peking University, Beijing, China 2Pengcheng Laboratory, Shenzhen, China. Correspondence to: Jiaying Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Encoder Side with DDIM. ... Algorithm 2 Decoder Side with DDIM. |
| Open Source Code | Yes | The project is at https://realpasu.github. io/Corr Diff_Website. |
| Open Datasets | Yes | We train all the models on the dataset of DIV2K (Agustsson & Timofte, 2017) which includes 800 high-resolution images. ... We evaluate our method on 3 datasets: Kodak (Kodak, 2024), CLIC professional (Toderici et al., 2020) and DIV2K-test (Agustsson & Timofte, 2017). |
| Dataset Splits | No | The paper mentions training on DIV2K and testing on DIV2K-test, Kodak, and CLIC professional datasets, but it does not explicitly provide details about a validation dataset split, such as specific percentages or sample counts for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions using PyTorch, DISTS, LPIPS, DDIM, and Adam optimizer but does not provide specific version numbers for these software dependencies (e.g., 'PyTorch 1.x' or 'Python 3.x'). |
| Experiment Setup | Yes | We first train only the score network for 400,000 iterations and then train the entire framework for another 400,000 iterations with a batch size of 8, learning rate of 5e-5 and optimizer of Adam (Kingma & Ba, 2014). ... We randomly crop them into 256x256 patches in the training process. |