Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Network…

PyTorch implementation of neural style transfer algorithm

PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!

Reconstruction of the original paper on neural style transfer (Gatys et al.). I've additionally included reconstruction scripts which allow you to reconstruct only the content or the style of the image - for better understanding of how NST works.

This is the Pytorch implementation of Universal Style Transfer via Feature Transforms.

Framework for Neural Style Transfer (NST) built upon PyTorch

[CVPR'20] Collaborative Distillation for Ultra-Resolution Universal Style Transfer (PyTorch)

[AAAI 2022] Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

UE5Dream - Local version

Unity Post Processing with Convolution Neural Network

Neural Style Transfer for Fluids

Reconstruction of the fast neural style transfer (Johnson et al.). Some portions of the paper have been improved by the follow-up work like the instance normalization, etc. Checkout transformer_net.py's header for details.

Official Implementation of Domain-Aware Universal Style Transfer

Awesome 3D Stylization - Advances in 3D Neural Stylization

PyTorch implementation of "Universal Style Transfer via Feature Trasforms"

Neural Style Transfer (pytorch ver.)

This is PyTorch Implementation of Neural Style Transfer Algorithm which is modified for Audios.

Create naive (no temporal loss) NST for videos with person segmentation. Just place your videos in data/, run and you get your stylized and segmented videos.

Code for "Jhamtani H.*, Gangal V.*, Hovy E. and Nyberg E. Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models" Workshop on Stylistic Variation, EMNLP 2017