mlsanityData to 7 Oct 2026

DR-GAN: Distribution Regularization for Text-to-Image Generation

arXiv 2204.07945 ↗First posted 17 Apr 2022cs.CVcited by 10

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Abstract (arXiv)

This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning. In DR-GAN, we introduce two novel modules: a Semantic Disentangling Module (SDM) and a Distribution Normalization Module (DNM). SDM combines the spatial self-attention mechanism and a new Semantic Disentangling Loss (SDL) to help the generator distill key semantic information for the image generation. DNM uses a Variational Auto-Encoder (VAE) to normalize and denoise the image latent distribution, which can help the discriminator better distinguish synthesized images from real images. DNM also adopts a Distribution Adversarial Loss (DAL) to guide the generator to align with normalized real image distributions in the latent space. Extensive experiments on two public datasets demonstrated that our DR-GAN achieved a competitive performance in the Text-to-Image task.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 36 of its 55 references that are in the map. First posted on arXiv 17 Apr 2022.

Builds on

36 of 55 references.

References · 36

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  7. 2018Group Normalizationcited by 1,514
  8. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187
  9. 2016Least Squares Generative Adversarial Networkscited by 1,000
  10. 2016Generative Adversarial Text to Image Synthesiscited by 735
  11. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  12. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
24 more
  1. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
  2. 2018Image Generation from Scene Graphscited by 324
  3. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  4. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  5. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  6. 2019Controllable Text-to-Image Generationcited by 187
  7. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  8. 2017Video Generation From Textcited by 153
  9. 2020Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Contentcited by 148
  10. 2018StoryGAN: A Sequential Conditional GAN for Story Visualizationcited by 137
  11. 2016Mode Regularized Generative Adversarial Networkscited by 123
  12. 2019Object-driven Text-to-Image Synthesis via Adversarial Trainingcited by 117
  13. 2018Inferring Semantic Layout for Hierarchical Text-to-Image Synthesiscited by 101
  14. 2018Human Semantic Parsing for Person Re-identificationcited by 100
  15. 2019Semantic Object Accuracy for Generative Text-to-Image Synthesiscited by 89
  16. 2018Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Networkcited by 64
  17. 2019Semantics Disentangling for Text-to-Image Generationcited by 61
  18. 2017Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Modelscited by 53
  19. 2019Generating Multiple Objects at Spatially Distinct Locationscited by 41
  20. 2019MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generationcited by 29
  21. 2020Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approachcited by 29
  22. 2019CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesiscited by 9
  23. 2018Text-to-image Synthesis via Symmetrical Distillation Networkscited by 6
  24. 2019Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensicscited by 6

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Cited by year

Citing papers per year: 2023: 4; 2024: 4; 2025: 2.

10 papers.

  1. 2024A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trendscited by 82
  2. 2023Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Surveycited by 10
  3. 2023Vision + Language Applications: A Surveycited by 8
  4. 2023ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesiscited by 4
  5. 2023RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignmentcited by 4
  6. 2024TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformerscited by 1
  7. 2024Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reductioncited by 1
  8. 2025End-to-end Training for Text-to-Image Synthesis using Dual-Text Embeddingscited by 1
  9. 2024Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathycited by 0
  10. 2025Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interactioncited by 0