Text SummarizerPython Framework for Extractive Text Summarization
Stars: ✭ 96 (-13.51%)
Mutual labels: text-summarization
Stylegan2 Projecting ImagesProjecting images to latent space with StyleGAN2.
Stars: ✭ 102 (-8.11%)
Mutual labels: generative-adversarial-network
TaganAn official PyTorch implementation of the paper "Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language", NeurIPS 2018
Stars: ✭ 97 (-12.61%)
Mutual labels: generative-adversarial-network
Lsd SegLearning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Stars: ✭ 99 (-10.81%)
Mutual labels: generative-adversarial-network
NatsrNatural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019)
Stars: ✭ 105 (-5.41%)
Mutual labels: generative-adversarial-network
Sprint ganPrivacy-preserving generative deep neural networks support clinical data sharing
Stars: ✭ 92 (-17.12%)
Mutual labels: generative-adversarial-network
Pytorch Gan TimeseriesGANs for time series generation in pytorch
Stars: ✭ 109 (-1.8%)
Mutual labels: generative-adversarial-network
SpectralnormalizationkerasSpectral Normalization for Keras Dense and Convolution Layers
Stars: ✭ 100 (-9.91%)
Mutual labels: generative-adversarial-network
Unet Stylegan2A Pytorch implementation of Stylegan2 with UNet Discriminator
Stars: ✭ 106 (-4.5%)
Mutual labels: generative-adversarial-network
Lggan[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Stars: ✭ 97 (-12.61%)
Mutual labels: generative-adversarial-network
Chemgan ChallengeCode for the paper: Benhenda, M. 2017. ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.
Stars: ✭ 98 (-11.71%)
Mutual labels: generative-adversarial-network
Doppelganger[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
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Mutual labels: generative-adversarial-network
ExermoteUsing Machine Learning to predict the type of exercise from movement data
Stars: ✭ 108 (-2.7%)
Mutual labels: generative-adversarial-network
PorousmediaganReconstruction of three-dimensional porous media using generative adversarial neural networks
Stars: ✭ 94 (-15.32%)
Mutual labels: generative-adversarial-network
DeliganThis project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.
Stars: ✭ 103 (-7.21%)
Mutual labels: generative-adversarial-network
Stylegan WebA web porting for NVlabs' StyleGAN.
Stars: ✭ 112 (+0.9%)
Mutual labels: generative-adversarial-network
TransformersumModels to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.
Stars: ✭ 107 (-3.6%)
Mutual labels: text-summarization
Pixel2style2pixelOfficial Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation"
Stars: ✭ 1,395 (+1156.76%)
Mutual labels: generative-adversarial-network