There are 0 repository under frechet-inception-distance topic.
High-fidelity performance metrics for generative models in PyTorch
Pytorch implementation of common GAN metrics
[CVPR 2024] On the Content Bias in Fréchet Video Distance
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.
Lots of evaluation metrics for the generative adversarial networks in pytorch
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
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.
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
Computing the Sliding Fréchet Inception Distance between fake and real images with continous labels
Official Repository for the paper "Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend".
Capturing the special characteristics of Claude Monet's paintings in order to turn ordinary pictures into similar style paintings
Implementation of GAN-based text-to-image models for a comparative study on the CUB and COCO datasets
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/)
The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN
GAN-based framework to generate depth images of infants from a desired image and pose
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.
Converting photos into Monet style paintings using CycleGANs with Differentiable Augmentation.