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..
When, Why, and Which Pretrained GANs Are Useful?
Authors: Timofey Grigoryev, Andrey Voynov, Artem Babenko
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our conclusions are confirmed by experiments with the state-of-the-art Style GAN2 (Karras et al., 2020b), chosen due to its practical importance and a variety of open-sourced checkpoints, which can be used as pretrained sources. |
| Researcher Affiliation | Collaboration | Timofey Grigoryev Yandex EMAIL Andrey Voynov Yandex EMAIL Artem Babenko Yandex EMAIL |
| Pseudocode | No | The paper describes methods in text and uses figures, but no formal pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | The code and pretrained models are available online at https://github.com/yandex-research/gan-transfer |
| Open Datasets | Yes | Table 5: Datasets information. CIFAR-104 https://www.cs.toronto.edu/~kriz/cifar.html FFHQ5 https://github.com/NVlabs/ffhq-dataset Flowers6 https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html Grumpy-Cat7 https://hanlab.mit.edu/projects/data-efficient-gans/datasets/ Imagenet8 https://image-net.org/index.html LSUN Bedroom9 https://www.yf.io/p/lsun LSUN Cat9 https://www.yf.io/p/lsun LSUN Church9 https://www.yf.io/p/lsun LSUN Dog9 https://www.yf.io/p/lsun Satellite-Buildings10 https://www.aicrowd.com/challenges/mapping-challenge-old Satellite-Landscapes11 https://earthview.withgoogle.com Simpsons12 https://www.kaggle.com/c/cmx-simpsons/data Bre Ca HAD13 https://figshare.com/articles/dataset/Bre_Ca_HAD_A_Dataset_for_Breast_Cancer_Histopathological_Annotation_and_Diagnosis/7379186 |
| Dataset Splits | Yes | As for LSUN-Bedroom, we split the original data into a train and a test subset in the proportion 9 : 1 and train e4e on the train set and evaluate on the test set. |
| Hardware Specification | Yes | Training is performed on eight Tesla V100 GPUs and takes approximately three hours per 1M real images shown to the discriminator. |
| Software Dependencies | No | We always use the official Py Torch implementation of Style GAN2-ADA (Karras et al., 2020a) provided by the authors1. (No specific version numbers for PyTorch or StyleGAN2-ADA are mentioned.) |
| Experiment Setup | Yes | We use the stylegan2 configuration in the ADA implementation with the default hyperparameters (same for all datasets). [...] In these experiments, we train for 25M real images shown to the discriminator. [...] We use batch size 64 and Adam optimizers with learning rate 0.0002 and β1, β2 = 0.5, 0.999. |