Papers › cGANs with Projection Discriminator

cGANs with Projection Discriminator

15 Feb 2018ICLR 2018 1arXiv:1802.05637archive 2025-07-28

Takeru Miyato, Masanori Koyama

We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator.

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Code

Syntology Ran 9 of 9 code samples harvested from 2 repositories linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · violated contract; 3 ran · our draft was wrong; 2 ran · fixture could not drive it; 1 ran with no contract checked.

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crcrpar/pytorch.sngan_projection officialmentioned in papermentioned on GitHubpytorch report
pfnet-research/sngan_projection officialmentioned in papertfNOASSERTION report
DanielLongo/AdversarialTrain mentioned on GitHubpytorch report
DanielLongo/GANs mentioned on GitHubpytorch report
DanielLongo/cGANs mentioned on GitHubpytorch report
XHChen0528/SNGAN_Projection_Pytorch mentioned on GitHubpytorchMIT report
alhasan-abdellatif/cgans mentioned on GitHubpytorch report
yuepingwang/sngan-projection mentioned on GitHubpytorch report

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1ran · honoured contract
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prepare_results_dir crcrpar/pytorch.sngan_projection/train_64.py official repository ran · fixture could not drive it MIT (permissive) · 71ca378125225f3b · report
sample_from_data crcrpar/pytorch.sngan_projection/train_64.py official repository ran · our draft was wrong MIT (permissive) · d01f1fbb0262694b · report
str2bool XHChen0528/SNGAN_Projection_Pytorch/parameter.py community (archive-listed) ran · violated contract MIT (permissive) · e5b1aff86a339d0e · report
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train_gan identical code first harvested elsewhere ran · metamorphic tier: deterministic licence of this copy not recorded · 2fb479c48e65d7b4 · report

Tasks

Conditional Image GenerationImage GenerationSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Conditional Image Generation CIFAR-10 Projection Discriminator FID 17.5 #15 of 25 Archive leaderboard report
Conditional Image Generation CIFAR-10 Projection Discriminator Inception score 8.62 #15 of 25 Archive leaderboard report
Conditional Image Generation ImageNet 128x128 Projection Discriminator FID 27.62 #21 of 22 Archive leaderboard report
Conditional Image Generation ImageNet 128x128 Projection Discriminator Inception score 36.8 #21 of 22 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: Projection Discriminator

1x1 ConvolutionAdamAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingKaiming InitializationMax PoolingProjection DiscriminatorReLUResidual BlockResidual ConnectionSpectral Normalization

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