cycle text2face: cycle text-to-face gan via transformers
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 9 Jun 2022.
Builds on
12 of 29 references.
References · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
- 2014Deep Learning Face Attributes in the Wildcited by 3,693
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
- 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
- 2018Pros and Cons of GAN Evaluation Measurescited by 224
- 2018An efficient framework for learning sentence representationscited by 198
- 2015Attribute2Image: Conditional Image Generation from Visual Attributescited by 93
- 2019Text2FaceGAN: Face Generation from Fine Grained Textual Descriptionscited by 9
- 2020Cycle Text-To-Image GAN with BERTcited by 2
Built on by
Not cited yet.