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..
Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation
Authors: Quang Nguyen, Truong Vu, Anh Tran, Khoi Nguyen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct evaluations on two datasets, PASCAL VOC and MSCOCO, and our approach significantly outperforms concurrent work. To evaluate the quality of the synthesized datasets, we introduce two benchmark datasets: synth-VOC and synth-COCO. We conduct all ablation study experiments on the text prompts described in Sec. 3.1. |
| Researcher Affiliation | Collaboration | Quang Nguyen1,2 Truong Vu1 Anh Tran1 Khoi Nguyen1 1Vin AI Research, 2Ho Chi Minh City University of Technology, VNU-HCM |
| Pseudocode | No | The paper describes procedures in text and uses equations, but there are no explicitly labeled "Pseudocode" or "Algorithm" blocks or figures. |
| Open Source Code | Yes | Our benchmarks and code will be released at https://github.com/Vin AIResearch/Dataset-Diffusion. |
| Open Datasets | Yes | We evaluate our Dataset Diffusion on two datasets: PASCAL VOC 2012 [10] and COCO 2017 [11]. |
| Dataset Splits | Yes | The PASCAL VOC 2012 dataset ... to have a total of 12, 046 training, 1, 449 validation, and 1, 456 test images. The COCO 2017 dataset contains 80 object classes and 1 background class with 118, 288 training and 5K validation images, along with provided captions for each image. |
| Hardware Specification | Yes | We conduct our experiments on NVIDIA A100 40G GPUs. |
| Software Dependencies | Yes | We build our framework on Py Torch deep learning framework [52] and Stable Diffusion [5] version 2.1-base with T = 100 timesteps. Regarding semantic segmenter, we employ the Deep Lab V3 [15] and Mask2Former [24] segmenter implemented in the MMSegmentation framework [53]. |
| Experiment Setup | Yes | We construct the masks using optimal values for τ, α, and β, which are defined in Sec. 6.2. We use the Adam W optimizer with a learning rate of 1e 4 and weight decay of 1e 4. For other hyper-parameters, we follow standard settings in MMSegmentation. |