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FA-GAN: Feature-Aware GAN for Text to Image Synthesis
[article]
2021
arXiv
pre-print
Text-to-image synthesis aims to generate a photo-realistic image from a given natural language description. Previous works have made significant progress with Generative Adversarial Networks (GANs). Nonetheless, it is still hard to generate intact objects or clear textures (Fig 1). To address this issue, we propose Feature-Aware Generative Adversarial Network (FA-GAN) to synthesize a high-quality image by integrating two techniques: a self-supervised discriminator and a feature-aware loss.
arXiv:2109.00907v1
fatcat:yzq3s4tbprg7xmslvhdpxtokni