CADA-GAN: Context-Aware GAN with Data Augmentation
This work addresses dataset limitations and feature selection challenges in child face generation, but it is incremental as it adapts an existing StyleGAN2-Ada model with added attention and augmentation.
The paper tackles the problem of generating child faces from limited datasets and high-dimensional features by proposing CADA-GAN, a context-aware GAN with data augmentation, which achieves the lowest Mean Squared Error Loss on latent feature representations and produces more robust images compared to baseline models.
Current child face generators are restricted by the limited size of the available datasets. In addition, feature selection can prove to be a significant challenge, especially due to the large amount of features that need to be trained for. To manage these problems, we proposed CADA-GAN, a \textbf{C}ontext-\textbf{A}ware GAN that allows optimal feature extraction, with added robustness from additional \textbf{D}ata \textbf{A}ugmentation. CADA-GAN is adapted from the popular StyleGAN2-Ada model, with attention on augmentation and segmentation of the parent images. The model has the lowest \textit{Mean Squared Error Loss} (MSEloss) on latent feature representations and the generated child image is robust compared with the one that generated from baseline models.