A curated list of GAN & Deepfake papers and repositories. ✔️ means implementation is available.

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

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

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

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

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

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

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

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

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

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

• ✔️ 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]

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?

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

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

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

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

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

• Google Deepfake Detection Dataset
• FaceForensics++ Dataset
• Facebook Deepfake Detection Challenge (DFDC) Dataset
• "SwapMe and Faceswap" dataset
• "Fake Faces in the Wild (FFW) dataset
• Tampered Face (TAMFA) Dataset
• Celeb-DF(v2) Celebrity Deepfake Dataset
• DeeperForensics-1.0
• Diverse Fake Face Dataset (DFFD)