PyTorch implementation of neural style transfer algorithm

Pytorch implementation from scratch of [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [Huang+, ICCV2017]]

Pytorch implementation of Noisy Student Training for Automatic Speech Recognition and Automatic Pronunciation Error Detection problem

A simple and minimalistic implementation of the fast neural style transfer method presented in "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" by Johnson et. al. (2016) 🏞

A bot for Telegram that can transfer style from one image to another

A simple and minimalistic PyTorch implementation of "A Neural Algorithm of Artistic Style" by Gatys et. al (2015) 🏞

This project focuses on Neural Style Transfer (NST), a technique that applies the style of one image to the content of another image, creating a new, stylized image. NST leverages deep learning models, particularly Convolutional Neural Networks (CNNs), to extract and combine the content and style features of images.

Reproduction of Neural Style Transfer

Implement the neural style transfer algorithm to generate novel artistic images using foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems. Creating digital art using Neural Network based Style Transfer.

Telegram Bot @stylizer_bot providing image style transfer using NST & CycleGAN

Tensorflow based Neural Style Transfer application

Neural-style transfer (NST) using Tensorflow.