# Stack GAN

This paper's model architecture has many components, so I thought it would be good to layout the specifics of the architecture before implementing it.

## Model Architecture

![](https://868646840-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LztfBhQUrZzyA7O_ZkJ%2Fuploads%2Fgit-blob-866f95d9d595f5dd0c4c595393330054f3fa8c0c%2Fstacked-gan.png?alt=media)

1. Stage-I GAN
2. Stage-2 GAN

## Stage-I GAN

**Input**: Text embedding of the text description $$(\varphi_t)$$

### Conditioning Augmentation (CA)

**Purpose**: Create $$\hat{c_0}$$ vector that captures the meaning of $$\varphi_t$$ with variations.

**Process**: $$\varphi_t$$ → FC layer → $$\mu_0, \sigma_0$$ → $$\mathcal{N}(\mu_0(\varphi_t),\sigma_0(\varphi_t))$$ → $$\hat{c_0}$$ sampled from this Gaussian distribution

**Output**:
