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A curated list of GAN & Deepfake papers and repositories.

Awesome-GANS-and-Deepfakes

Awesome Maintenance made-with-Markdown Documentation Status

A curated list of GAN & Deepfake papers and repositories. βœ”οΈ means implementation is available.

GANs

Tl;dr GANs containg two competing neural networks which iteratively generate new data with the same statistics as the training set.

Unconditional GANs

  • βœ”οΈ Vanilla GAN: Generative Adversarial Networks, [paper], [github]
  • βœ”οΈ DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, [paper], [github]
  • βœ”οΈ WGAN: Wasserstein GAN, [paper], [github]
  • βœ”οΈ WGAN-GP: Improved Training of Wasserstein GANs, [paper], [github]
  • βœ”οΈ RGAN: The relativistic discriminator: a key element missing from standard GAN, [paper], [github]
  • βœ”οΈ BGAN: Boundary-Seeking Generative Adversarial Networks, [paper], [github]
  • βœ”οΈ ClusterGAN: Latent Space Clustering in Generative Adversarial Networks, [paper], [github]

Conditional GANs

  • βœ”οΈ CGAN: Conditional Generative Adversarial Nets, [paper], [github]
  • βœ”οΈ ACGAN: Conditional Image Synthesis With Auxiliary Classifier GANs, [paper], [github]
  • βœ”οΈ CCGAN: Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks, [paper], [github]

Image-to-Image Translation

  • βœ”οΈ CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [paper], [github]
  • βœ”οΈ StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, [paper], [github]
  • βœ”οΈ Pix2Pix: Image-to-Image Translation with Conditional Adversarial Nets, [paper], [github]
  • βœ”οΈ DualGAN: Unsupervised Dual Learning for Image-to-Image Translation, [paper], [github]
  • βœ”οΈ BicycleGAN: Toward Multimodal Image-to-Image Translation, [paper], [github]

Volumetric (3D) Generation

  • βœ”οΈ 3DGAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling, [paper], [github]
  • βœ”οΈ Inverse Graphics GAN: Inverse Graphics GAN - Learning to Generate 3D Shapes from Unstructured 2D Data, [paper], [github]

Applications using GANs

Anime generator

  • Towards the Automatic Anime Characters Creation with Generative Adversarial Networks, [paper]
  • βœ”οΈ [Project] Keras-GAN-Animeface-Character, [github]

Interactive Image generation

  • βœ”οΈ Generative Visual Manipulation on the Natural Image Manifold, [paper], [github]
  • βœ”οΈ Neural Photo Editing with Introspective Adversarial Networks, [paper], [github]

3D Object generation

  • 3D Shape Induction from 2D Views of Multiple Objects, [paper]
  • βœ”οΈ Parametric 3D Exploration with Stacked Adversarial Networks, [github], [youtube]
  • βœ”οΈ Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes, [github], [blog]

Super-resolution

  • βœ”οΈ Image super-resolution through deep learning, [github]
  • βœ”οΈ Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [paper], [github]
  • High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks, [paper]
  • βœ”οΈ Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, [paper], [github]
  • βœ”οΈ ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, [paper], [github]
  • βœ”οΈ MUNIT: Multimodal Unsupervised Image-to-Image Translation, [paper], [github]
  • βœ”οΈ SRGAN: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [paper], [github]

Image Inpainting (hole filling)

  • βœ”οΈ Context Encoders: Feature Learning by Inpainting, [paper], [github]
  • βœ”οΈ Semantic Image Inpainting with Perceptual and Contextual Losses, [paper], [github]
  • βœ”οΈ Generative Face Completion, [paper], [github]

Medical Image Segmentation

  • βœ”οΈ Vox2Vox: 3D-GAN for Brain Tumor Segmentation, [paper], [github]
  • SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation, [paper]
  • Generative Adversarial Neural Networks for Pigmented and Non-Pigmented Skin Lesions Detection in Clinical Images, [paper]

Deepfakes

Tl;dr Deepfakes are fake videos or audio recordings that look and sound just like the real thing. Watch this video of Obama speaking... or was that really him?

CNN-based Face-swapping

  • βœ”οΈ Fast Face-swap Using Convolutional Neural Networks, [paper], [github]
  • βœ”οΈ DeepFaceLab: A simple, flexible and extensible face swapping framework, [paper], [github]

GAN-based Face-swapping

  • βœ”οΈ Fewshot Face Translation GAN, [github]
  • Faceswap-GAN, [github]
  • βœ”οΈ AttGAN: Facial Attribute Editing by Only Changing What You Want, [paper], [github]
  • MulGAN: Facial Attribute Editing by Exemplar, [paper]
  • βœ”οΈ MaskGAN: Towards Diverse and Interactive Facial Image Manipulation, [paper], [github]
  • βœ”οΈ StarGAN v2: Diverse Image Synthesis for Multiple Domains, [paper], [github]
  • βœ”οΈ FSGAN: Subject Agnostic Face Swapping and Reenactment, [paper], [github]

Deepfake Detection

CNN-based methods

  • βœ”οΈ MesoNet [paper], [github]
  • Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches, [paper]
  • Deep Fake Image Detection Based on Pairwise Learning, [paper]

RCN-based methods

  • Recurrent Convolutional Strategies for Face Manipulation Detection in Videos, [paper]

Other ML methods

  • SVM: Exposing Deep Fakes Using Inconsistent Head Poses, [paper]

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