StackGAN: Facial Image Generation Optimizations
This work addresses facial image generation for computer vision applications, but it is incremental as it builds on existing StackGAN methods with subpar results compared to state-of-the-art models.
The paper tackled the problem of computationally expensive and unstable photorealistic image generation by proposing a variant of the StackGAN architecture that generates grayscale facial images in two stages, achieving FID scores of 73 for edge images and 59 for grayscale images on the CelebA dataset.
Current state-of-the-art photorealistic generators are computationally expensive, involve unstable training processes, and have real and synthetic distributions that are dissimilar in higher-dimensional spaces. To solve these issues, we propose a variant of the StackGAN architecture. The new architecture incorporates conditional generators to construct an image in many stages. In our model, we generate grayscale facial images in two different stages: noise to edges (stage one) and edges to grayscale (stage two). Our model is trained with the CelebA facial image dataset and achieved a Fréchet Inception Distance (FID) score of 73 for edge images and a score of 59 for grayscale images generated using the synthetic edge images. Although our model achieved subpar results in relation to state-of-the-art models, dropout layers could reduce the overfitting in our conditional mapping. Additionally, since most images can be broken down into important features, improvements to our model can generalize to other datasets. Therefore, our model can potentially serve as a superior alternative to traditional means of generating photorealistic images.