Papers › Quaternion Generative Adversarial Networks
Quaternion Generative Adversarial Networks
Eleonora Grassucci, Edoardo Cicero, Danilo Comminiello
Latest Generative Adversarial Networks (GANs) are gathering outstanding results through a large-scale training, thus employing models composed of millions of parameters requiring extensive computational capabilities. Building such huge models undermines their replicability and increases the training instability. Moreover, multi-channel data, such as images or audio, are usually processed by realvalued convolutional networks that flatten and concatenate the input, often losing intra-channel spatial relations. To address these issues related to complexity and information loss, we propose a family of quaternion-valued generative adversarial networks (QGANs). QGANs exploit the properties of quaternion algebra, e.g., the Hamilton product, that allows to process channels as a single entity and capture internal latent relations, while reducing by a factor of 4 the overall number of parameters. We show how to design QGANs and to extend the proposed approach even to advanced models.We compare the proposed QGANs with real-valued counterparts on several image generation benchmarks. Results show that QGANs are able to obtain better FID scores than real-valued GANs and to generate visually pleasing images. Furthermore, QGANs save up to 75% of the training parameters. We believe these results may pave the way to novel, more accessible, GANs capable of improving performance and saving computational resources.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | CIFAR-10 | QSNGAN | FID | 31.966 | #69 of 78 | Archive leaderboard | report |
| Image Generation | CelebA-HQ 128x128 | QSNGAN | FID | 29.417 | #7 of 7 | Archive leaderboard | report |
| Image Generation | CelebA-HQ 128x128 | QSNGAN | IS | 2.249 | #7 of 7 | Archive leaderboard | report |
| Image Generation | Oxford 102 Flowers 128x128 | QSNGAN | FID | 115.838 | #1 of 1 | Archive leaderboard | report |
| Image Generation | Oxford 102 Flowers 128x128 | QSNGAN | IS | 3 | #1 of 1 | Archive leaderboard | report |
| Image Generation | STL-10 | QSNGAN | FID | 59.611 | #30 of 31 | Archive leaderboard | report |
| Image Generation | STL-10 | QSNGAN | Inception score | 4.987 | #30 of 31 | 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.
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