GroupDNet
Group Decreasing Network
**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](https://paperswithcode.com/method/vae) and SPADE, the encoder $E$ produces a
latent code $Z$ that is supposed to follow a Gaussian distribution $\mathcal{N}(0,1)$ 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.
Semantically Multi-modal Image Synthesis
https://paperswithcode.com/paper/semantically-mutil-modal-image-synthesis
Semantically Multi-modal Image Synthesis