We lifted 11 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| POSTECH-CVLab/PyTorch-StudioGAN | canonical | 1 of 1 |
| lyqcom/biggan | pwc_unofficial | 5 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| D_arch | Ran | lyqcom/biggan/model/BigGAN.py code served (permissive licence) · get_code("85eb95cce203dfc7") |
| cal_fid | Ran | lyqcom/biggan/src/inception_utils.py code served (permissive licence) · get_code("d441601e14f2c498") |
| find_classes | Ran | lyqcom/biggan/src/datasets.py code served (permissive licence) · get_code("130fb10627e87139") |
| is_image_file | Ran | lyqcom/biggan/src/datasets.py code served (permissive licence) · get_code("39565434228935b8") |
| make_dataset | Ran | lyqcom/biggan/src/datasets.py code served (permissive licence) · get_code("c92e605aa8f6c735") |
| normalize_2nd_moment | Ran | POSTECH-CVLab/PyTorch-StudioGAN/src/models/stylegan2.py pointer only (licence: NOASSERTION) · get_code("670fe68b890c9792") |
| CAL_FID | Not yet run | lyqcom/biggan/src/inception_utils.py code served (permissive licence) · get_code("645f242bb645d145") |
| G_arch | Not yet run | lyqcom/biggan/model/BigGAN.py code served (permissive licence) · get_code("44d6a6343f6eb182") |
| add_sample_parser | Not yet run | lyqcom/biggan/src/utils.py code served (permissive licence) · get_code("c4171e8c1ba5ea65") |
| numpy_calculate_frechet_distance | Not yet run | lyqcom/biggan/src/inception_utils.py code served (permissive licence) · get_code("69b2abc7f7010394") |
| update_config_roots | Not yet run | lyqcom/biggan/src/utils.py code served (permissive licence) · get_code("e6f2eaa40b13c353") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for realistic image synthesis. While training and evaluating GAN becomes increasingly important, the current GAN research ecosystem does not provide reliable benchmarks for which the evaluation is conducted consistently and fairly. Furthermore, because there are few validated GAN implementations, researchers devote considerable time to reproducing baselines. We study the taxonomy of GAN approaches and present a new open-source library named StudioGAN. StudioGAN supports 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 12 regularization modules, 3 differentiable augmentations, 7 evaluation metrics, and 5 evaluation backbones. With our training and evaluation protocol, we present a large-scale benchmark using various datasets (CIFAR10, ImageNet, AFHQv2, FFHQ, and Baby/Papa/Granpa-ImageNet) and 3 different evaluation backbones (InceptionV3, SwAV, and Swin Transformer). Unlike other benchmarks used in the GAN community, we train representative GANs, including BigGAN and StyleGAN series in a unified training pipeline and quantify generation performance with 7 evaluation metrics. The benchmark evaluates other cutting-edge generative models (e.g., StyleGAN-XL, ADM, MaskGIT, and RQ-Transformer). StudioGAN provides GAN implementations, training, and evaluation scripts with the pre-trained weights. StudioGAN is available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2206.09479")
get_code_for_paper("2206.09479")
have("2206.09479")
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