Neural Style Transfer based on Andy Warhol’s Monroe Diptych with pre-trained computer vision network VGG19, transfer learning, and…
About two months ago, I wrote an article on Neural Style Transfer where we transfer van Gogh’s Unique Style to any photo with Magenta’s Arbitrary Image Stylization Network using TensorFlow. It showed how to quickly you can apply Neural Style Transfer without doing any fine-tuning.
Neural style transfer is a method to blend two images and create a new image from a content image by copying the style of another image, called style image. This newly created image is often referred to as the stylized image.
After receiving positive comments and a tweet by the official TensorFlow account, I have decided to prepare another tutorial. This tutorial is relatively more advanced compared to the first article. In this article, we will copy Andy Warhol’s style in Marilyn Diptych to our own photos. Warhol created the Monroe Diptych in 1962 by painting the canvas first using different colors before screening Marilyn's now-famous image on top of the canvas. Although Warhol is not the founder of pop art, he is one of the most influential figures in the world of pop art.
On the tutorial's technical aspect, instead of using the out-of-the-box Magenta network, we will use the pre-trained computer vision model VGG-19 and fine-tune it. Therefore, this article is a transfer learning tutorial as well as a computer vision. Using the power of transfer learning, we can achieve better scores if we can properly tune the model and have a wide range of more customization options.
A comparison between models. Art Style Transfer consists in the transformation of an image into a similar one that seems to have been painted by an artist. Let's dive into art style transfer using neural networks.
Google Reveals "What is being Transferred” in Transfer Learning. Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community.
4 Pre-Trained CNN Models to Use for Computer Vision with Transfer Learning. Using State-of-the-Art Pre-trained Neural Network Models to Tackle Computer Vision Problems with Transfer Learning
A few compelling reasons for you to starting learning Computer. In today’s world, Computer Vision technologies are everywhere.
A practical and hands-on example to know how to use transfer learning using TensorFlow. We will learn how to use transfer learning for a classification task.