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- 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
- The GAN is dead; long live the GAN! A Modern Baseline GAN (R3GAN) - GitHub
Code for NeurIPS 2024 paper - The GAN is dead; long live the GAN! A Modern Baseline GAN - by Huang et al - brownvc R3GAN
- 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 - 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 - Yangyangii GAN-Tutorial: Simple Implementation of many GAN . . .
Simple Implementation of many GAN models with PyTorch - Yangyangii GAN-Tutorial
- 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 - dorarad gansformer: Generative Adversarial Transformers
Vanilla GAN: --baseline GAN, a standard GAN without style modulation StyleGAN2: --baseline StyleGAN2, with one global latent that modulates the image features k-GAN: --baseline kGAN, which generates multiple image layers independetly and then merge them into one shared image (supported only in the TF version)
- 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)
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