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DRAW: A Recurrent Neural Network For Image Generation

arXiv 1502.04623 ↗First posted 16 Feb 2015cs.CV cs.LG cs.NEcited by 190

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Abstract (arXiv)

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

Key facts. Cited by 190 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2015.

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Citing papers per year: 2021: 67; 2022: 43; 2023: 31; 2024: 26; 2025: 13; 2026: 10.

190 papers.

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  1. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  2. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
  3. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  4. 2023Human Preference Score: Better Aligning Text-to-Image Models with Human Preferencecited by 284
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  6. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  7. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  8. 2023JourneyDB: A Benchmark for Generative Image Understandingcited by 203
  9. 2022Scalable Adaptive Computation for Iterative Generationcited by 190
  10. 2021Fast Convergence of DETR with Spatially Modulated Co-Attentioncited by 176
  11. 2021Paint by Wordcited by 107
  12. 2021Generalization and Robustness Implications in Object-Centric Learningcited by 81
38 more
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  4. 2024PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinementcited by 61
  5. 2022Improving Object-centric Learning with Query Optimizationcited by 60
  6. 2021SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editingcited by 58
  7. 2024Any-Resolution AI-Generated Image Detection by Spectral Learningcited by 56
  8. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  9. 2021Consistency Regularization for Variational Auto-Encoderscited by 50
  10. 2023TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesiscited by 48
  11. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  12. 2021DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesiscited by 45
  13. 2021Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Conceptscited by 40
  14. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  15. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  16. 2022Few-Shot Diffusion Modelscited by 37
  17. 2022Content and Style Aware Generation of Text-line Images for Handwriting Recognitioncited by 35
  18. 2021Multimodal Dialogue Response Generationcited by 32
  19. 2022VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programmingcited by 31
  20. 2021Copyright in Generative Deep Learningcited by 29
  21. 2021Unsupervised Object Learning via Common Fatecited by 29
  22. 2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
  23. 2023UniGS: Unified Representation for Image Generation and Segmentationcited by 29
  24. 2023Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problemscited by 28
  25. 2023Differential Diffusion: Giving Each Pixel Its Strengthcited by 27
  26. 2021Position, Padding and Predictions: A Deeper Look at Position Information in CNNscited by 26
  27. 2021Sign Language Production: A Reviewcited by 26
  28. 2021FoveaTer: Foveated Transformer for Image Classificationcited by 26
  29. 2021Unsupervised Foreground Extraction via Deep Region Competitioncited by 26
  30. 2021Conditional Image Generation by Conditioning Variational Auto-Encoderscited by 25
  31. 2023AI-Generated Content (AIGC) for Various Data Modalities: A Surveycited by 25
  32. 2021Compositional Transformers for Scene Generationcited by 24
  33. 2023Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomycited by 24
  34. 2021A survey on Variational Autoencoders from a GreenAI perspectivecited by 22
  35. 2024Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integrationcited by 22
  36. 2021Creativity and Machine Learning: A Surveycited by 21
  37. 2021Cascaded Cross MLP-Mixer GANs for Cross-View Image Translationcited by 21
  38. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20

50 most cited of 190 All 190 (JSON)