What are the main features of orobix/visual-feature-attribution-using-wasserstein-gans-pytorch?

The main features of orobix/visual-feature-attribution-using-wasserstein-gans-pytorch are: Computer Vision Research, Generative Models, Model Implementations.

Visual Feature Attribution Using Wasserstein GANs Pytorch

Name: orobix/visual-feature-attribution-using-wasserstein-gans-pytorch
Author: orobix

This code aims to reproduce results obtained in the paper "Visual Feature Attribution using Wasserstein GANs" (official repo, TensorFlow code)

Features

Computer Vision Research - Visual feature attribution using Wasserstein GANs.
Generative Models - Visual feature attribution using WGANs.
Model Implementations - Visual feature attribution using GANs.

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

This code aims to reproduce results obtained in the paper "Visual Feature Attribution using Wasserstein GANs" (official repo, TensorFlow code)

Features

Computer Vision Research - Visual feature attribution using Wasserstein GANs.
Generative Models - Visual feature attribution using WGANs.
Model Implementations - Visual feature attribution using GANs.

yunjey/stargan

5,292View on GitHub

StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the

DmitryUlyanov/deep-image-prior

8,085View on GitHub

This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning. The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recove

arunmallya/packnet

243View on GitHub

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

alexis-jacq/Pytorch-Sketch-RNN

0View on GitHub

orobixVisual-Feature-Attribution-Using-Wasserstein-GANs-Pytorch