Inception score for measuring quality of images generating from GAN
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Updated
Dec 12, 2017 - Python
Inception score for measuring quality of images generating from GAN
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10
Lots of evaluation metrics for the generative adversarial networks in pytorch
CPU/GPU/TPU implementation of the Inception Score
Metrics to evaluate GAN
IS, FID score Pytorch and TF implementation, TF implementation is a wrapper of the official ones.
GAN-based framework to generate depth images of infants from a desired image and pose
Implementation of GAN-based text-to-image models for a comparative study on the CUB and COCO datasets
An unofficial Pytorch implementation of SNGAN, achieving IS of 8.21 and FID of 14.21 on CIFAR-10.
A pip-installable evaluator for GANs (IS and FID). Accepts either dataloaders or individual batches. Supports on-the-fly evaluation during training. A working DCGAN SVHN demo script provided.
Pytorch implementation of popular generative models
This GitHub repository contains an implementation of Deep Convolutional Generative Adversarial Networks (DCGAN) for image generation. With this project, you can generate stunning and realistic images using the power of deep learning.
CXR-ACGAN: Auxiliary Classifier GAN (AC-GAN) for Chest X-Ray (CXR) Images Generation (Pneumonia, COVID-19 and healthy patients) for the purpose of data augmentation. Implemented in TensorFlow, trained on COVIDx CXR-3 dataset.
High-fidelity performance metrics for generative models in PyTorch
Compute FID scores with PyTorch.
PyTorch implementation of 'DDPM' (Ho et al., 2020) and training it on CelebA 64×64
Pytorch implementation of common image generation metrics.
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