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..
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Authors: Zhisheng Xiao, Karsten Kreis, Arash Vahdat
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity competitive with original diffusion models while being 2000 faster on the CIFAR-10 dataset. |
| Researcher Affiliation | Collaboration | Zhisheng Xiao The University of Chicago EMAIL Karsten Kreis NVIDIA EMAIL Arash Vahdat NVIDIA EMAIL |
| Pseudocode | No | The paper describes methods and processes in text but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page and code: https://nvlabs.github.io/denoising-diffusion-gan. |
| Open Datasets | Yes | Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity competitive with original diffusion models while being 2000 faster on the CIFAR-10 dataset. We train our model on datasets with larger images, including Celeb A-HQ (Karras et al., 2018) and LSUN Church (Yu et al., 2015) at 256 256px resolution. |
| Dataset Splits | No | The paper evaluates on datasets like CIFAR-10 but does not specify the explicit training, validation, and test splits (e.g., percentages or exact counts) used for these datasets within the paper. |
| Hardware Specification | Yes | When evaluating sampling time, we use models trained on CIFAR-10 and generate a batch of 100 images on a V100 GPU. We train our models on CIFAR-10 using 4 V100 GPUs. On Celeb A-HQ and LSUN Church we use 8 V100 GPUs. |
| Software Dependencies | Yes | We use Pytorch 1.9.0 and CUDA 11.0. |
| Experiment Setup | Yes | For all datasets, we set the number of diffusion steps to be 4. Initial learning rate for discriminator 10^-4, Initial learning rate for generator 1.6 x 10^-4 (or 2 x 10^-4 for LSUN Church) Adam optimizer beta1 0.5, Adam optimizer beta2 0.9, EMA 0.9999 (or 0.999), Batch size 256 (or 32, 64), # of training iterations 400k (or 750k, 600k). |