Methods › Computer Vision › Generative Models › StyleALAE
StyleALAE
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
StyleALAE is a type of adversarial latent autoencoder that uses a StyleGAN based generator. For this the latent space 𝒲 plays the same role as the intermediate latent space in StyleGAN. Therefore, the G network becomes the part of StyleGAN depicted on the right side of the Figure. The left side is a novel architecture that we designed to be the encoder E. The StyleALAE encoder has Instance Normalization (IN) layers to extract multiscale style information that is combined into a latent code w via a learnable multilinear map.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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One-shot domain adaptation for semantic face editing of real world images using StyleALAE 31 Aug 2021 · 0 repositories · arXiv:2108.13876
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Adversarial Latent Autoencoders 9 Apr 2020 · 12 repositories · arXiv:2004.04467Syntology ran 0 of 2 samples · 2 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Disentanglement | 1 |
| Domain Adaptation | 1 |
| Image Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections