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A Simple and Effective Baseline for Attentional Generative Adversarial Networks

arXiv 2306.14708 ↗First posted 26 Jun 2023cs.CVcited by 0

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Abstract (arXiv)

Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve the performance of the generator and the quality of image generation, and Stack-GAN++, which gradually improves the details and quality of the image by stacking multiple generators and discriminators. However, this series of improvements to GAN all have redundancy to a certain extent, which affects the generation performance and complexity to a certain extent. We use the popular simple and effective idea (1) to remove redundancy structure and improve the backbone network of AttnGAN. (2) to integrate and reconstruct multiple losses of DAMSM. Our improvements have significantly improved the model size and training efficiency while ensuring that the model's performance is unchanged and finally proposed our SEAttnGAN. Code is avalilable at https://github.com/jmyissb/SEAttnGAN.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 37 references that are in the map. First posted on arXiv 26 Jun 2023.

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References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
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  6. 2022Generative Adversarial Networkscited by 5,627
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  11. 2018YOLOv3: An Incremental Improvementcited by 2,659
  12. 2014Conditional Generative Adversarial Netscited by 2,162
18 more
  1. 2016Conditional Image Generation with PixelCNN Decoderscited by 862
  2. 2016Generative Adversarial Text to Image Synthesiscited by 735
  3. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  4. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
  5. 2015A Neural Algorithm of Artistic Stylecited by 483
  6. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  7. 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
  8. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  9. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
  10. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  11. 2016Learning What and Where to Drawcited by 105
  12. 2018Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Languagecited by 90
  13. 2017Semantic Image Synthesis via Adversarial Learningcited by 87
  14. 2019Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANscited by 79
  15. 2019Semantics Disentangling for Text-to-Image Generationcited by 61
  16. 2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
  17. 2021DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesiscited by 45
  18. 2020TIME: Text and Image Mutual-Translation Adversarial Networkscited by 9

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