Bibliography:

  1. ​ ‘GAN’ tag

  2. ​ ‘StyleGAN anime’ tag

  3. ​ ‘ProGAN’ tag

  4. ​ ‘BigGAN’ tag

  5. ​ ‘CLIP’ tag

  6. ​ ‘video generation’ tag

  7. ​ The GAN is dead; long live the GAN! R3GAN: A Modern GAN Baseline

  8. ​ Machine Learning as a Tool for Hypothesis Generation

  9. ​ The spontaneous emergence of ‘a sense of beauty’ in untrained deep neural networks

  10. ​ Generative Models: What do they know? Do they know things? Let’s find out!

  11. ​ Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models

  12. ​ Rosetta Neurons: Mining the Common Units in a Model Zoo

  13. ​ Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

  14. ​ Drag Your GAN (DragGAN): Interactive Point-based Manipulation on the Generative Image Manifold

  15. ​ Realistic Face Reconstruction from Deep Embeddings

  16. ​ SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

  17. ​ StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

  18. ​ Brain2GAN: Reconstructing perceived faces from the primate brain via StyleGAN3

  19. ​ Fast text2StyleGAN: Text-Free Learning of a Natural Language Interface for Pretrained Face Generators

  20. ​ User-Controllable Latent Transformer for StyleGAN Image Layout Editing

  21. ​ Generator Knows What Discriminator Should Learn in Unconditional GANs

  22. ​ CelebV-HQ: A Large-Scale Video Facial Attributes Dataset

  23. ​ InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images

  24. ​ BigVGAN: A Universal Neural Vocoder with Large-Scale Training

  25. ​ Diffusion-GAN: Training GANs with Diffusion

  26. ​ StyleGAN-Human: A Data-Centric Odyssey of Human Generation

  27. ​ Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values

  28. ​ State-of-the-Art in the Architecture, Methods and Applications of StyleGAN

  29. ​ AI-synthesized faces are indistinguishable from real faces and more trustworthy

  30. ​ StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

  31. ​ StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN-2

  32. ​ Efficient Geometry-Aware 3D Generative Adversarial Networks

  33. ​ CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions

  34. ​ HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

  35. ​ LAFITE: Towards Language-Free Training for Text-to-Image Generation

  36. ​ Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers

  37. ​ StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis

  38. ​ StyleAlign: Analysis and Applications of Aligned StyleGAN Models

  39. ​ Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN

  40. ​ Controlled GAN-Based Creature Synthesis via a Challenging Game Art Dataset—Addressing the Noise-Latent Trade-Off

  41. ​ LARGE: Latent-Based Regression through GAN Semantics

  42. ​ Alias-Free Generative Adversarial Networks

  43. ​ Lazy, a tool for running things in idle time

  44. ​ Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

  45. ​ Explaining in Style: Training a GAN to explain a classifier in StyleSpace

  46. ​ DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

  47. ​ Labels4Free: Unsupervised Segmentation using StyleGAN

  48. ​ Repurposing GANs for One-shot Semantic Part Segmentation

  49. ​ Generative Adversarial Transformers

  50. ​ Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

  51. ​ not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution

  52. ​ Rewriting a Deep Generative Model

  53. ​ Differentiable Augmentation for Data-Efficient GAN Training

  54. ​ StyleGAN2-ADA: Training Generative Adversarial Networks with Limited Data

  55. ​ On Data Augmentation for GAN Training

  56. ​ Image Augmentations for GAN Training

  57. ​ Ambigrammatic Figures: 55 Grotesque Ambigrams

  58. ​ Practical aspects of StyleGAN2 training

  59. ​ GANSpace: Discovering Interpretable GAN Controls

  60. ​ Evolving Normalization-Activation Layers

  61. ​ Top-K Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

  62. ​ Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

  63. ​ Conditional Image Generation and Manipulation for User-Specified Content § Pg3

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  65. ​ Analyzing and Improving the Image Quality of StyleGAN

  66. ​ Detecting GAN generated errors

  67. ​ Seeing What a GAN Cannot Generate

  68. ​ Stabilizing Generative Adversarial Networks: A Survey

  69. ​ Interpreting the Latent Space of GANs for Semantic Face Editing

  70. ​ On the "steerability" of generative adversarial networks

  71. ​ Style Generator Inversion for Image Enhancement and Animation

  72. ​ NoGAN: Decrappification, DeOldification, and Super Resolution

  73. ​ Improved Precision and Recall Metric for Assessing Generative Models

  74. ​ Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?

  75. ​ The Machine As Author

  76. ​ MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks

  77. ​ This Person Does Not Exist

  78. ​ FIGR: Few-shot Image Generation with Reptile

  79. ​ A Style-Based Generator Architecture for Generative Adversarial Networks

  80. ​ GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

  81. ​ Discriminator Rejection Sampling

  82. ​ On Self Modulation for Generative Adversarial Networks

  83. ​ Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

  84. ​ Inverting The Generator Of A Generative Adversarial Network (II)

  85. ​ Megapixel Size Image Creation using Generative Adversarial Networks

  86. ​ AdaIN: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

  87. ​ StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

  88. ​ Ukiyo-e Search

  89. ​ Deep Danbooru

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  91. ​ WatchGAN: Advancing Generated Watch Images With StyleGANs

  92. ​ Generating New Watch Designs With StyleGAN

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  153. ​ Random Walk StyleGAN

  154. ​ I Just Tried My StyleGAN Layer Swapping Method the Other Way round to What I’d Been Doing Before. So Making the Ukiyo-E Model Human (rather Than the Other Way Around) and I Love the Results!

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  269. Wikipedia Bibliography:

    1. ​ Alexei A. Efros

    2. ​ Naoki Murata

    3. ​ Richard Baraniuk

    4. ​ Ivan Skorokhodov

    5. ​ Leonidas Guibas

    6. ​ Jonathan Tremblay

    7. ​ Peter Wonka