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CADA-GAN: Context-Aware GAN with Data Augmentation

arXiv 2301.08849 ↗First posted 21 Jan 2023cs.CVcited by 0

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Abstract (arXiv)

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 Context-Aware GAN that allows optimal feature extraction, with added robustness from additional Data Augmentation. 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 textitMean Squared Error Loss (MSEloss) on latent feature representations and the generated child image is robust compared with the one that generated from baseline models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 21 Jan 2023.

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26 of 44 references.

References · 26

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14 more
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