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Text-To-Image with Generative Adversarial Networks

arXiv 2410.08608 ↗First posted 11 Oct 2024cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Generating realistic images from human texts is one of the most challenging problems in the field of computer vision (CV). The meaning of descriptions given can be roughly reflected by existing text-to-image approaches. In this paper, our main purpose is to propose a brief comparison between five different methods base on the Generative Adversarial Networks (GAN) to make image from the text. In addition, each model architectures synthesis images with different resolution. Furthermore, the best and worst obtained resolutions is 64*64, 256*256 respectively. However, we checked and compared some metrics that introduce the accuracy of each model. Also, by doing this study, we found out the best model for this problem by comparing these different approaches essential metrics.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 11 Oct 2024.

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13 of 20 references.

References · 13

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  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  9. 2016Generative Adversarial Text to Image Synthesiscited by 735
  10. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  11. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
  12. 2017Semantic Image Synthesis via Adversarial Learningcited by 87
1 more
  1. 2018Text-to-image Synthesis via Symmetrical Distillation Networkscited by 6

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