Frechet Inception Distance for Keras-based GANs

This 'Generative Adversarial Network' project was implemented in grad course CSE-676 : Deep Learning [Fall 2019 @UB_SUNY] Course Instructor : Sargur N. Srihari(https://cedar.buffalo.edu/~srihari/)

Lots of evaluation metrics for the generative adversarial networks in pytorch

IS, FID score Pytorch and TF implementation, TF implementation is a wrapper of the official ones.

PyTorch implementation of WGAN-GP-based video generation. Includes functionality for measuring Frechet Video Distance and implementing recent research improvements of WGAN-GP. Read paper at https://github.com/talcron/frame-prediction-pytorch/blob/media/paper.pdf

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

Converting photos into Monet style paintings using CycleGANs with Differentiable Augmentation.

Capturing the special characteristics of Claude Monet's paintings in order to turn ordinary pictures into similar style paintings

This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman

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.

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.

The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN

FID computation in Jax/Flax.

Pytorch implementation of common GAN metrics

A generative adversarial network engineered that utilizes a discriminator and a generator. The GAN can be trained using a Binary Cross Entropy Loss or a Wasserstein Distance Loss to generate replicate images based on input data.

High-fidelity performance metrics for generative models in PyTorch

Pytorch implementation of Visual DNA, an approach to represent and compare images.

Computing the Sliding Fréchet Inception Distance between fake and real images with continous labels

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]