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 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.