Image Style Transfer Using CNN
Armaan Khan, Ankit Kumar, Nilima Kulkarni, Anirudh Kamat, Deepak Kumar · Zenodo (CERN European Organization for Nuclear Research) · 2021
Image style transfer was a really difficult task earlier because image processing takes a lot of amounts of computational power. As the technology is improving day by day due to which machines with higher computations are available easily and thus the image processing task is made easier than before. And as for the image style transfer, we have to learn features from the image where the size of the images could be very high so computation increases significantly. But now various improvements are made on Convolutional Neural Networks (CNNs) and with the help of transfer learning we already have pretrained models like VGGnet which is a 19 layers deep neural network architecture to work on, with the help of which we are saved from writing everything from the scratch. Image style transfer is basically an algorithm which uses Convolutional Neural Networks to learn features from two different images and mixes them together in a new image which is basically a combination of the two input images.