added checkmarks for available implementations

README.md

@@ -10,54 +10,54 @@ A curated list of GAN & Deepfake papers and repositories. :heavy_check_mark: mea
 Tl;dr GANs containg two competing neural networks which iteratively generate new data with the same statistics as the training set.
 
 ### Unconditional GANs
-+ Vanilla GAN: Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1406.2661), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/tree/master/implementations/gan)
-+ DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1511.06434), [[github]](https://github.com/carpedm20/DCGAN-tensorflow)
-+ WGAN: Wasserstein GAN, [[paper]](https://arxiv.org/abs/1701.07875), [[github]](https://github.com/martinarjovsky/WassersteinGAN)
-+ WGAN-GP: Improved Training of Wasserstein GANs, [[paper]](https://arxiv.org/pdf/1704.00028.pdf), [[github]](https://github.com/caogang/wgan-gp)
-+ RGAN: The relativistic discriminator: a key element missing from standard GAN, [[paper]](https://arxiv.org/abs/1807.00734), [[github]](https://github.com/AlexiaJM/RelativisticGAN)
++ :heavy_check_mark: Vanilla GAN: Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1406.2661), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/tree/master/implementations/gan)
++ :heavy_check_mark: DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1511.06434), [[github]](https://github.com/carpedm20/DCGAN-tensorflow)
++ :heavy_check_mark: WGAN: Wasserstein GAN, [[paper]](https://arxiv.org/abs/1701.07875), [[github]](https://github.com/martinarjovsky/WassersteinGAN)
++ :heavy_check_mark: WGAN-GP: Improved Training of Wasserstein GANs, [[paper]](https://arxiv.org/pdf/1704.00028.pdf), [[github]](https://github.com/caogang/wgan-gp)
++ :heavy_check_mark: RGAN: The relativistic discriminator: a key element missing from standard GAN, [[paper]](https://arxiv.org/abs/1807.00734), [[github]](https://github.com/AlexiaJM/RelativisticGAN)
 
 ### Conditional GANs
-+ CGAN: Conditional Generative Adversarial Nets, [[paper]](https://arxiv.org/abs/1411.1784), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/cgan/cgan.py)
-+ ACGAN: Conditional Image Synthesis With Auxiliary Classifier GANs, [[paper]](https://arxiv.org/abs/1610.09585), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/acgan/acgan.py)
++ :heavy_check_mark: CGAN: Conditional Generative Adversarial Nets, [[paper]](https://arxiv.org/abs/1411.1784), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/cgan/cgan.py)
++ :heavy_check_mark: ACGAN: Conditional Image Synthesis With Auxiliary Classifier GANs, [[paper]](https://arxiv.org/abs/1610.09585), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/acgan/acgan.py)
 
 ### Image-to-Image Translation
-+ CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [[paper]](https://arxiv.org/abs/1703.10593), [[github]](https://github.com/junyanz/CycleGAN)
-+ StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1711.09020), [[github]](https://github.com/yunjey/stargan)
-+ Pix2Pix: Image-to-Image Translation with Conditional Adversarial Nets, [[paper]](https://arxiv.org/abs/1611.07004), [[github]](https://github.com/phillipi/pix2pix)
++ :heavy_check_mark: CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [[paper]](https://arxiv.org/abs/1703.10593), [[github]](https://github.com/junyanz/CycleGAN)
++ :heavy_check_mark: StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1711.09020), [[github]](https://github.com/yunjey/stargan)
++ :heavy_check_mark: Pix2Pix: Image-to-Image Translation with Conditional Adversarial Nets, [[paper]](https://arxiv.org/abs/1611.07004), [[github]](https://github.com/phillipi/pix2pix)
 
 ### Volumetric (3D) Generation
-+ 3DGAN: Learning a Probabilistic Latent Space of Object Shapes
++ :heavy_check_mark: 3DGAN: Learning a Probabilistic Latent Space of Object Shapes
 via 3D Generative-Adversarial Modeling, [[paper]](http://3dgan.csail.mit.edu/papers/3dgan_nips.pdf), [[github]](https://github.com/enochkan/3dgan-keras)
-+ Inverse Graphics GAN: Inverse Graphics GAN - Learning to Generate 3D Shapes from Unstructured 2D Data, [[paper]](https://arxiv.org/pdf/2002.12674.pdf), [[github]](https://github.com/BraneShop/showreel/issues/504)
++ :heavy_check_mark: Inverse Graphics GAN: Inverse Graphics GAN - Learning to Generate 3D Shapes from Unstructured 2D Data, [[paper]](https://arxiv.org/pdf/2002.12674.pdf), [[github]](https://github.com/BraneShop/showreel/issues/504)
 
 ## Applications using GANs
 
 ### Anime generator
 + Towards the Automatic Anime Characters Creation with Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1708.05509)
-+ [Project] Keras-GAN-Animeface-Character, [[github]](https://github.com/forcecore/Keras-GAN-Animeface-Character)
++ :heavy_check_mark: [Project] Keras-GAN-Animeface-Character, [[github]](https://github.com/forcecore/Keras-GAN-Animeface-Character)
 
 ### Interactive Image generation
-+ Generative Visual Manipulation on the Natural Image Manifold, [[paper]](https://arxiv.org/pdf/1609.03552), [[github]](https://github.com/junyanz/iGAN)
-+ Neural Photo Editing with Introspective Adversarial Networks, [[paper]](http://arxiv.org/abs/1609.07093), [[github]](https://github.com/ajbrock/Neural-Photo-Editor)
++ :heavy_check_mark: Generative Visual Manipulation on the Natural Image Manifold, [[paper]](https://arxiv.org/pdf/1609.03552), [[github]](https://github.com/junyanz/iGAN)
++ :heavy_check_mark: Neural Photo Editing with Introspective Adversarial Networks, [[paper]](http://arxiv.org/abs/1609.07093), [[github]](https://github.com/ajbrock/Neural-Photo-Editor)
 
 ### 3D Object generation
 + 3D Shape Induction from 2D Views of Multiple Objects, [[paper]](https://arxiv.org/pdf/1612.05872.pdf)
-+ Parametric 3D Exploration with Stacked Adversarial Networks, [[github]](https://github.com/maxorange/pix2vox), [[youtube]](https://www.youtube.com/watch?v=ITATOXVvWEM)
-+ Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes, [[github]](https://github.com/yunishi3/3D-FCR-alphaGAN), [[blog]](https://becominghuman.ai/3d-multi-object-gan-7b7cee4abf80)
++ :heavy_check_mark: Parametric 3D Exploration with Stacked Adversarial Networks, [[github]](https://github.com/maxorange/pix2vox), [[youtube]](https://www.youtube.com/watch?v=ITATOXVvWEM)
++ :heavy_check_mark: Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes, [[github]](https://github.com/yunishi3/3D-FCR-alphaGAN), [[blog]](https://becominghuman.ai/3d-multi-object-gan-7b7cee4abf80)
 
 ### Super-resolution
-+ Image super-resolution through deep learning, [[github]](https://github.com/david-gpu/srez)
-+ Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [[paper]](https://arxiv.org/abs/1609.04802), [[github]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)
++ :heavy_check_mark: Image super-resolution through deep learning, [[github]](https://github.com/david-gpu/srez)
++ :heavy_check_mark: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [[paper]](https://arxiv.org/abs/1609.04802), [[github]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)
 + High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1707.00737.pdf)
-+ Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, [[paper]](https://arxiv.org/pdf/1811.00344.pdf), [[github]](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw)
++ :heavy_check_mark: Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, [[paper]](https://arxiv.org/pdf/1811.00344.pdf), [[github]](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw)
 
 ### Image Inpainting (hole filling)
-+ Context Encoders: Feature Learning by Inpainting, [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf), [[github]](https://github.com/pathak22/context-encoder)
-+ Semantic Image Inpainting with Perceptual and Contextual Losses, [[paper]](https://arxiv.org/abs/1607.07539), [[github]](https://github.com/bamos/dcgan-completion.tensorflow)
-+ Generative Face Completion, [[paper]](https://drive.google.com/file/d/0B8_MZ8a8aoSeenVrYkpCdnFRVms/edit), [[github]](https://github.com/Yijunmaverick/GenerativeFaceCompletion)
++ :heavy_check_mark: Context Encoders: Feature Learning by Inpainting, [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf), [[github]](https://github.com/pathak22/context-encoder)
++ :heavy_check_mark: Semantic Image Inpainting with Perceptual and Contextual Losses, [[paper]](https://arxiv.org/abs/1607.07539), [[github]](https://github.com/bamos/dcgan-completion.tensorflow)
++ :heavy_check_mark: Generative Face Completion, [[paper]](https://drive.google.com/file/d/0B8_MZ8a8aoSeenVrYkpCdnFRVms/edit), [[github]](https://github.com/Yijunmaverick/GenerativeFaceCompletion)
 
 ### Medical Image Segmentation
-+ Vox2Vox: 3D-GAN for Brain Tumor Segmentation, [[paper]](https://arxiv.org/abs/2003.13653), [[github]](https://github.com/enochkan/vox2vox)
++ :heavy_check_mark: Vox2Vox: 3D-GAN for Brain Tumor Segmentation, [[paper]](https://arxiv.org/abs/2003.13653), [[github]](https://github.com/enochkan/vox2vox)
 + SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation, [[paper]](https://arxiv.org/abs/1706.01805)
 + Generative Adversarial Neural Networks for Pigmented and Non-Pigmented Skin Lesions Detection in Clinical Images, [[paper]](https://ieeexplore.ieee.org/document/7968584/)
 
@@ -65,23 +65,23 @@ via 3D Generative-Adversarial Modeling, [[paper]](http://3dgan.csail.mit.edu/pap
 Tl;dr Deepfakes are fake videos or audio recordings that look and sound just like the real thing.
 
 ### CNN-based Face-swapping
-+ Fast Face-swap Using Convolutional Neural Networks, [[paper]](https://arxiv.org/abs/1611.09577), [[github]](https://github.com/deepfakes/faceswap#overview)
-+ DeepFaceLab: A simple, flexible and extensible face
++ :heavy_check_mark: Fast Face-swap Using Convolutional Neural Networks, [[paper]](https://arxiv.org/abs/1611.09577), [[github]](https://github.com/deepfakes/faceswap#overview)
++ :heavy_check_mark: DeepFaceLab: A simple, flexible and extensible face
 swapping framework, [[paper]](https://arxiv.org/pdf/2005.05535v4.pdf), [[github]](https://github.com/iperov/DeepFaceLab)
 
 ### GAN-based Face-swapping
-+ Fewshot Face Translation GAN, [[github]](https://github.com/shaoanlu/fewshot-face-translation-GAN)
++ :heavy_check_mark: Fewshot Face Translation GAN, [[github]](https://github.com/shaoanlu/fewshot-face-translation-GAN)
 + Faceswap-GAN, [[github]](https://github.com/shaoanlu/faceswap-GAN)
-+ AttGAN: Facial Attribute Editing by Only
++ :heavy_check_mark: AttGAN: Facial Attribute Editing by Only
 Changing What You Want, [[paper]](http://vipl.ict.ac.cn/uploadfile/upload/2019112511573287.pdf), [[github]](https://github.com/LynnHo/AttGAN-Tensorflow)
 + MulGAN: Facial Attribute Editing by Exemplar, [[paper]](https://arxiv.org/abs/1912.12396)
-+ MaskGAN: Towards Diverse and Interactive Facial Image Manipulation, [[paper]](https://arxiv.org/abs/1907.11922), [[github]](https://github.com/switchablenorms/CelebAMask-HQ)
-+ StarGAN v2: Diverse Image Synthesis for Multiple Domains, [[paper]](https://arxiv.org/abs/1912.01865), [[github]](https://github.com/clovaai/stargan-v2)
++ :heavy_check_mark: MaskGAN: Towards Diverse and Interactive Facial Image Manipulation, [[paper]](https://arxiv.org/abs/1907.11922), [[github]](https://github.com/switchablenorms/CelebAMask-HQ)
++ :heavy_check_mark: StarGAN v2: Diverse Image Synthesis for Multiple Domains, [[paper]](https://arxiv.org/abs/1912.01865), [[github]](https://github.com/clovaai/stargan-v2)
 
 ## Deepfake Detection
 
 ### CNN-based methods
-+ MesoNet [[paper]](https://arxiv.org/abs/1809.00888), [[github]](https://github.com/HongguLiu/MesoNet-Pytorch)
++ :heavy_check_mark: MesoNet [[paper]](https://arxiv.org/abs/1809.00888), [[github]](https://github.com/HongguLiu/MesoNet-Pytorch)
 + Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches, [[paper]](https://www.ohadf.com/papers/AgarwalFaridFriedAgrawala_CVPRW2020.pdf)
 + Deep Fake Image Detection Based on Pairwise Learning, [[paper]](https://www.mdpi.com/2076-3417/10/1/370)