Methods › Computer Vision › Image Generation Models › GroupDNet
Group Decreasing Network
GroupDNet
Introduced by Zhen Zhu et al. in Semantically Multi-modal Image Synthesis
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Group Decreasing Network, or GroupDNet, is a type of convolutional neural network for multi-modal image synthesis. GroupDNet contains one encoder and one decoder. Inspired by the idea of VAE and SPADE, the encoder E produces a latent code Z that is supposed to follow a Gaussian distribution 𝒩(0,1) during training. While testing, the encoder E is discarded. A randomly sampled code from the Gaussian distribution substitutes for Z. To fulfill this, the re-parameterization trick is used to enable a differentiable loss function during training. Specifically, the encoder predicts a mean vector and a variance vector through two fully connected layers to represent the encoded distribution. The gap between the encoded distribution and Gaussian distribution can be minimized by imposing a KL-divergence loss.
Papers archive 2025-07-28
1 shown of 1, 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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Semantically Multi-modal Image Synthesis 28 Mar 2020 · 1 repository · arXiv:2003.12697
Tasks archive 2025-07-28
2 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 |
|---|---|
| Decoder | 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
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