Papers › Instance-Conditioned GAN
Instance-Conditioned GAN
Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michal Drozdzal, Adriana Romero-Soriano
Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging in unconditional settings. In this paper, we take inspiration from kernel density estimation techniques and introduce a non-parametric approach to modeling distributions of complex datasets. We partition the data manifold into a mixture of overlapping neighborhoods described by a datapoint and its nearest neighbors, and introduce a model, called instance-conditioned GAN (IC-GAN), which learns the distribution around each datapoint. Experimental results on ImageNet and COCO-Stuff show that IC-GAN significantly improves over unconditional models and unsupervised data partitioning baselines. Moreover, we show that IC-GAN can effortlessly transfer to datasets not seen during training by simply changing the conditioning instances, and still generate realistic images. Finally, we extend IC-GAN to the class-conditional case and show semantically controllable generation and competitive quantitative results on ImageNet; while improving over BigGAN on ImageNet-LT. Code and trained models to reproduce the reported results are available at https://github.com/facebookresearch/ic_gan.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conditional Image Generation | ImageNet 128x128 | IC-GAN + DA | FID | 9.5 | #15 of 22 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 128x128 | IC-GAN + DA | Inception score | 108.6 | #15 of 22 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 256x256 | BigGAN+ [Brock et al.] (chx96) | FID | 8.1 | #4 of 5 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 256x256 | BigGAN+ [Brock et al.] (chx96) | Inception score | 144.2 | #4 of 5 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 256x256 | IC-GAN (chx96) + DA | FID | 8.2±0.1 | #5 of 5 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 256x256 | IC-GAN (chx96) + DA | Inception score | 173.8±0.9 | #5 of 5 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 64x64 | IC-GAN + DA | FID | 6.7 | #1 of 4 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 64x64 | IC-GAN + DA | Inception score | 45.9±0.3 | #1 of 4 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 64x64 | BigGAN* [Brock et al.] +DA | FID | 10.2±0.1 | #4 of 4 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 64x64 | BigGAN* [Brock et al.] +DA | Inception score | 30.1±0.1 | #4 of 4 | 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
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