Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Authors: Zhisheng Xiao, Karsten Kreis, Arash Vahdat
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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 zxiao@uchicago.edu Karsten Kreis NVIDIA kkreis@nvidia.com Arash Vahdat NVIDIA avahdat@nvidia.com |
| 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). |