mlsanityData to 7 Oct 2026

Diverse Single Image Generation with Controllable Global Structure

arXiv 2102.04780 ↗First posted 9 Feb 2021cs.CV eess.IVcited by 2

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Abstract (arXiv)

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for mainly capturing the patch statistics and, thereby, not being able to capture global statistics very well. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. Our results are visually better than the state-of-the-art particularly in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 38 of its 51 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

38 of 51 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  6. 2017Squeeze-and-Excitation Networkscited by 4,763
  7. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  8. 2014Going Deeper with Convolutionscited by 4,231
  9. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  10. 2016Improved Techniques for Training GANscited by 2,938
  11. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  12. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
26 more
  1. 2017Improved Training of Wasserstein GANscited by 2,223
  2. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  3. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  4. 2017Deep Image Priorcited by 1,097
  5. 2018Self-Attention Generative Adversarial Networkscited by 809
  6. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
  7. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  8. 2017"Zero-Shot" Super-Resolution using Deep Internal Learningcited by 285
  9. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  10. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
  11. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  12. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
  13. 2019Blind Super-Resolution Kernel Estimation using an Internal-GANcited by 175
  14. 2015Human Pose Estimation with Iterative Error Feedbackcited by 121
  15. 2018"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priorscited by 114
  16. 2018GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkscited by 107
  17. 2017DeLiGAN : Generative Adversarial Networks for Diverse and Limited Datacited by 77
  18. 2019Diversity-Sensitive Conditional Generative Adversarial Networkscited by 77
  19. 2020Improved Techniques for Training Single-Image GANscited by 77
  20. 2018Patch-Based Image Inpainting with Generative Adversarial Networkscited by 70
  21. 2018Non-Stationary Texture Synthesis by Adversarial Expansioncited by 66
  22. 2017Learning Texture Manifolds with the Periodic Spatial GANcited by 53
  23. 2016Texture Synthesis with Spatial Generative Adversarial Networkscited by 45
  24. 2020Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Samplecited by 43
  25. 2015Iterative Instance Segmentationcited by 19
  26. 2018Adversarial Feedback Loopcited by 8

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.

2 papers.

  1. 2021ExSinGAN: Learning an Explainable Generative Model from a Single Imagecited by 12
  2. 2024Content-Aware Preserving Image Generationcited by 0