Generative adversarial networks 101Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.
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Genre ShapehdCode and Data Release for GenRe (NeurIPS 2018) and ShapeHD (ECCV 2018)
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Gan MriCode repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'
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conv3d-video-action-recognitionMy experimentation around action recognition in videos. Contains Keras implementation for C3D network based on original paper "Learning Spatiotemporal Features with 3D Convolutional Networks", Tran et al. and it includes video processing pipelines coded using mPyPl package. Model is being benchmarked on popular UCF101 dataset and achieves result…
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GdwctOfficial PyTorch implementation of GDWCT (CVPR 2019, oral)
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pytorch-ganGAN model using PyTorch
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Keras SrganPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
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cDCGANPyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)
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Repo 2018Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core
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Pose2poseThis is a pix2pix demo that learns from pose and translates this into a human. A webcam-enabled application is also provided that translates your pose to the trained pose. Everybody dance now !
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Keras-Generating-Sentences-from-a-Continuous-SpaceText Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349
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Zi2ziLearning Chinese Character style with conditional GAN
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chiA high-level framework for advanced deep learning with TensorFlow
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SteganoganSteganoGAN is a tool for creating steganographic images using adversarial training.
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alphaGANA PyTorch implementation of alpha-GAN
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gan-vae-pretrained-pytorchPretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
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GDPPGenerator loss to reduce mode-collapse and to improve the generated samples quality.
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gan deeplearning4jAutomatic feature engineering using Generative Adversarial Networks using Deeplearning4j and Apache Spark.
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