论文标题
循环文本2：通过变压器循环文本到面gan
cycle text2face: cycle text-to-face gan via transformers
论文作者
论文摘要
文本到脸部是文本对图像的子集，由于其更详细的生产，需要更复杂的体系结构。在本文中，我们提出了一个称为Cycle Text2face的编码器模型。 Cycle Text2Face是编码器部分中的一项新计划，它使用句子变压器和GAN生成文本描述的图像。该周期是通过在模型的解码器部分中复制面部文本来完成的。使用Celeba数据集评估模型，比以前的基于GAN的模型可以带来更好的结果。在测量生成面的质量（除了满足人类受众群体外，我们还获得3.458的FID得分。该模型具有高速处理，可在短时间内提供优质的面部图像。
Text-to-face is a subset of text-to-image that require more complex architecture due to their more detailed production. In this paper, we present an encoder-decoder model called Cycle Text2Face. Cycle Text2Face is a new initiative in the encoder part, it uses a sentence transformer and GAN to generate the image described by the text. The Cycle is completed by reproducing the text of the face in the decoder part of the model. Evaluating the model using the CelebA dataset, leads to better results than previous GAN-based models. In measuring the quality of the generate face, in addition to satisfying the human audience, we obtain an FID score of 3.458. This model, with high-speed processing, provides quality face images in the short time.
