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..
Diffusion-based Image Translation using disentangled style and content representation
Authors: Gihyun Kwon, Jong Chul Ye
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks. |
| Researcher Affiliation | Academia | Gihyun Kwon1, Jong Chul Ye2,1 Department of Bio and Brain Engineering1, Kim Jaechul Graduate School of AI2, KAIST cyclomon,EMAIL |
| Pseudocode | Yes | A.4 ALGORITHM For detailed explanation, we include Algorithm of our proposed image translation mathods in Algorithm 1. |
| Open Source Code | Yes | Our detailed implementation can be found in our official Git Hub repository.1 |
| Open Datasets | Yes | All experiments were performed using unconditional score model pre-trained with Imagenet 256 256 resolution datasets (Dhariwal & Nichol (2021)). For our quantitative results using text-guided image translation, we used two different datasets Animals and Landscapes. |
| Dataset Splits | No | The paper does not specify explicit train/validation/test dataset splits with percentages or counts, nor does it refer to predefined splits for training and validation purposes. |
| Hardware Specification | Yes | The generation process takes 40 seconds per image in single RTX 3090 unit. All experiments are conducted with single RTX3090 GPU, on the same hardware and software environment. |
| Software Dependencies | No | The paper mentions using pre-trained models and referring to official source code of other methods but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | In all the experiments, we used diffusion step of T = 60 and the resampling repetition of N = 10; therefore, the total of 70 diffusion reverse steps are used. For hyperparameters, we use λ1 = 200, λ2 = 100, λ3 = 2000, λ4 = 1000, λ5 = 200. For imageguided translation, we set λmse = 1.5. For our CLIP loss, we set λs = 0.4 and λi = 0.2. |