PyTorch-GAN - GitHub
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. In the adversarial learning of N real training samples and M generated samples, the target of discriminator training is to distribute all the probability mass to the real samples, each ...
gan · GitHub Topics · GitHub
2024年8月24日 · 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 …
tensorflow/gan: Tooling for GANs in TensorFlow - GitHub
TF-GAN is composed of several parts, which are designed to exist independently: Core : the main infrastructure needed to train a GAN. Set up training with any combination of TF-GAN library calls, custom-code, native TF code, and other frameworks
generative-adversarial-network · GitHub Topics · GitHub
2024年7月3日 · 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 …
Picsart-AI-Research/MI-GAN - GitHub
This repository is the official implementation of MI-GAN. MI-GAN: A Simple Baseline for Image Inpainting on Mobile Devices Andranik Sargsyan, Shant Navasardyan, Xingqian Xu, Humphrey Shi | [Supplementary] Our method MI-GAN can produce plausible results both on complex scene images as well as on face images.
GitHub - Yangyangii/GAN-Tutorial: Simple Implementation of …
Simple Implementation of many GAN models with PyTorch. Topics pytorch gan mnist infogan dcgan regularization celeba wgan began wgan-gp infogan-pytorch conditional-gan pytorch-gan gan-implementations vanilla-gan gan-pytorch gan …
DF-GAN: A Simple and Effective Baseline for Text-to-Image
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis (CVPR 2022 Oral) Official Pytorch implementation for our paper DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis by Ming Tao , Hao Tang , Fei Wu , Xiao-Yuan Jing , Bing-Kun Bao , Changsheng Xu .
tkarras/progressive_growing_of_gans - GitHub
Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset. ★★★ NEW: StyleGAN2-ADA-PyTorch is now available; see the …
starter from "How to Train a GAN?" at NIPS2016 - GitHub
In GAN papers, the loss function to optimize G is min (log 1-D), but in practice folks practically use max log D. because the first formulation has vanishing gradients early on; Goodfellow et. al (2014) In practice, works well: Flip labels when training generator: real = fake, fake = real
GitHub - amanchadha/coursera-gan-specialization: Programming ...
Build a more sophisticated GAN using convolutional layers. Learn about useful activation functions, batch normalization, and transposed convolutions to tune your GAN architecture and apply them to build an advanced DCGAN specifically for processing images. Assignment: Deep Convolutional GAN (DCGAN)
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