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-GAN: Training GANs with Diffusion
Authors: Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the advantages of Diffusion-GAN over strong GAN baselines on various datasets, showing that it can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs. ... We conduct extensive experiments to answer the following questions: (a) Will Diffusion-GAN outperform state-of-the-art GAN baselines on benchmark datasets? (b) Will the diffusion-based noise injection help the learning of GANs in domain-agnostic tasks? (c) Will our method improve the performance of data-efficient GANs trained with a very limited amount of data? |
| Researcher Affiliation | Collaboration | 1The University of Texas at Austin, 2Microsoft Azure AI |
| Pseudocode | Yes | We provide the Diffusion-GAN algorithm in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Datasets. We conduct experiments on image datasets ranging from low-resolution (e.g., 32 32) to high-resolution (e.g., 1024 1024) and from low-diversity to high-diversity: CIFAR-10 (Krizhevsky, 2009), STL-10 (Coates et al., 2011), LSUN-Bedroom (Yu et al., 2015), LSUN-Church (Yu et al., 2015), AFHQ(Cat/Dog/Wild) (Choi et al., 2020), and FFHQ (Karras et al., 2019). |
| Dataset Splits | No | The paper mentions using well-known datasets and their training subsets, but does not explicitly provide details about specific training/validation/test splits (e.g., percentages or exact counts for each split). |
| Hardware Specification | Yes | We run all our experiments with either 4 or 8 NVIDIA V100 GPUs depending on the demands of the inherited training configurations. |
| Software Dependencies | No | The paper mentions using base GAN implementations (Style GAN2, Projected GAN, Ins Gen) but does not provide specific version numbers for software dependencies like PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | Diffusion config. For our diffusion-based noise injection, we set up a linearly increasing schedule for βt, where t {1, 2, . . . , T}. For pixel level injection in Style GAN2, we follow Ho et al. (2020b) and set β0 = 0.0001 and βT = 0.02. We adaptively modify T ranging from Tmin = 5 to Tmax = 1000. The image pixels are usually rescaled to [ 1, 1] so we set the Guassian noise standard deviation σ = 0.05. For feature level injection in Diffusion Projected GAN, we set β0 = 0.0001, βT = 0.01, Tmin = 5, Tmax = 500, and σ = 0.5. |