When, Why, and Which Pretrained GANs Are Useful?
Authors: Timofey Grigoryev, Andrey Voynov, Artem Babenko
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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 grigorev.ta@phystech.edu Andrey Voynov Yandex an.voynov@yandex.ru Artem Babenko Yandex artem.babenko@phystech.edu |
| 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. |