PyTorch-Multi-Style-Transfer
Style Transfer Library
A PyTorch implementation of neural style transfer and MSG-Net models for real-time image stylization
Neural Style and MSG-Net
981 stars
11 watching
206 forks
Language: Jupyter Notebook
last commit: almost 3 years ago deep-neural-networksreal-timestyle-transfer
Related projects:
Repository | Description | Stars |
---|---|---|
| An algorithm to combine the content of one image with the style of another image using convolutional neural networks | 841 |
| Tools and implementations for neural style transfer using PyTorch | 422 |
| This project allows users to apply the style of one image to another using deep learning techniques. | 82 |
| An introduction to PyTorch through the Neural-Style algorithm for image transformation | 136 |
| A deep learning algorithm for transferring the style of one image to another. | 428 |
| Software framework implementing neural style transfer with PyTorch support | 35 |
| This repository provides a PyTorch implementation of an image manipulation technique using a pretrained StyleGAN model. | 380 |
| Transfers artistic styles between images using neural networks and matrix operations | 1,538 |
| A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 |
| A PyTorch implementation of an image-to-image translation model that generates new images from paired training data. | 1,491 |
| An implementation of a deep learning-based makeup transfer algorithm that can robustly handle pose and expression variations in images. | 737 |
| An implementation of an image-to-image translation algorithm using deep learning and PyTorch | 428 |
| Adaptive style transfer in deep learning using a single model to apply multiple styles | 115 |
| An implementation of cascaded refinement networks for generating photorealistic images from semantic layouts | 65 |
| A PyTorch implementation of cycle and semi-supervised GANs for domain transfer between MNIST and SVHN datasets | 433 |