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gan · GitHub Topics · GitHub gan Generative adversarial networks (GAN) are a class of generative machine learning frameworks A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training
GitHub - eriklindernoren PyTorch-GAN: PyTorch implementations of . . . Softmax GAN is a novel variant of Generative Adversarial Network (GAN) The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross-entropy loss in the sample space of one single batch
GitHub - tensorflow gan: Tooling for GANs in TensorFlow TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs) Can be installed with pip using pip install tensorflow-gan, and used with import tensorflow_gan as tfgan Well-tested examples Interactive introduction to TF-GAN in
generative-adversarial-network · GitHub Topics · GitHub Generative adversarial networks (GAN) are a class of generative machine learning frameworks A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset
GitHub - HRLTY TP-GAN: Official TP-GAN Tensorflow implementation for . . . Official TP-GAN Tensorflow implementation for the ICCV17 paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis" by Huang, Rui and Zhang, Shu and Li, Tianyu and He, Ran The goal is to recover a frontal face image of the same person from a single face image under any poses
GitHub - yfeng95 GAN: Resources and Implementations of Generative . . . Wasserstein GAN stabilize the training by using Wasserstein-1 distance GAN before using JS divergence has the problem of non-overlapping, leading to mode collapse and convergence difficulty Use EM distance or Wasserstein-1 distance, so GAN solve the two problems above without particular architecture (like dcgan)