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gan · GitHub Topics · GitHub GitHub is where people build software More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects
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
GitHub - ratschlab RGAN: Recurrent (conditional) generative adversarial . . . Idea: Use generative adversarial networks (GANs) to generate real-valued time series, for medical purposes As the title suggests The GAN is R GAN because it uses recurrent neural networks for both encoder and decoder (specifically LSTMs)
GitHub - poloclub ganlab: GAN Lab: An Interactive, Visual . . . GAN Lab is a novel interactive visualization tool for anyone to learn and experiment with Generative Adversarial Networks (GANs), a popular class of complex deep learning models With GAN Lab, you can interactively train GAN models for 2D data distributions and visualize their inner-workings
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)
GAN生成对抗网络D_loss和G_loss到底应该怎样变化？ - 知乎 做 GAN 有一段时间了，可以回答下这个问题。 G是你的任务核心，最后推理用的也是G，所以G的LOSS是要下降收敛接近0的，G的目标是要欺骗到D。 而成功的训练中，由于要达到G欺骗D的目的，所以D的Loss是不会收敛的，在G欺骗D的情况下，D的LOSS会在0 5左右。
GitHub - Hzzone DU-GAN: DU-GAN: Generative Adversarial Networks with . . . DU-GAN This repository contains the PyTorch implementation of the paper: DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising accepted by IEEE Transactions on Instrumentation Measurement 2021