I’m sure if you’re an artist, you must have thought, What if I can paint like Picasso? Well, Deep Learning comes with an interesting Neural Style Transfer solution to answer this question. Neural Style Transfer is the art of creating a style for any content, in Layman’s terms.
The content is the layout or sketch, and the painting or the colors are the styles. It is an application of Deep Learning to transform images. The style representation and content representations can be separated in a CNN, learned during a computer vision task, such as an image recognition task.
To transfer styles from a given image to another, NST utilises a pre-trained convolutional neural network (CNN). by defining a loss feature that attempts to minimise the differences that will be discussed in detail later between a content image, a style image, and a generated image.
On the fundamental principle of convolution, ConvNets operate. For instance, let’s say we have an image and a filter. The filter is slid over the image and the weighted sum of the inputs covered by the filter is taken as output, transformed by a nonlinearity such as sigmoid or ReLU or tanh. Each filter has its own set of weights that do not change during the procedure of convolution.
Neural Style Transfer
Neural style transfer is a technique of optimization uses upto three images namely, a content image, a style reference image (such as the artwork of a famous painter), and the input image. that you want to style and mix them together so that the input image is transformed to look like the image of the content. but in the style of the image.
These algorithms are extremely flexible and have resulted in very creative and unique results due to the virtually infinite possible combinations of content and style.
We define a pre-trained convolutional model and loss function that visually blends two images, so we would need the following inputs.
Requirements:
- A Content Image -a picture to which we transfer style
- A Style Image – The style that we want to pass on
- An Input Image(generated) -The final content plus the style image required
In fact, one company adapted the algorithm and released a mobile application called Prisma, which uses the transfer of neural styles to apply artistic styles to images taken from your mobile. Is basically an AI power filter app.
How it works in a layman’s words?
Two distance functions are define by the principle of neural style transfer, one that describes how different the contents of two images are, content , and one that describes the difference between the two images, Lstyle, in terms of their style. Then to minimise the content distance with the content image and its style distance with the style image, we try to transform the input image, given three images, The desired image of the style, the desired image of the content and the input image (initialised with the content image).
In short, we’re going to take the base input image, the content image we want to match, and the image of style we want to match. By minimising the content and style distances (losses) with backpropagation, we’ll transform the base input image, creating an image that matches the content of the content image and the style of the style image.
Is it like a real art?
Well once we create the model, you will see that creating non-photo-realistic images with Neural Style Transfer is a very simple task. By mixing beautiful photos with the paintings of talented artists, you can create a lot of samples. Due to the little work the creator needs to add to the end product, there has been a discussion about whether these outputs are regarded as art. In the comments section, feel free to build the model, generate your samples, and share your thoughts.
Conclusion :
One of the most creative applications of convolutional neural networks is the transfer of neural styles. The neural network can recombine the content and the style image to effectively create an artistic image by taking a content image and a style image.
Written by: Deepti Sardar
Reviewed By: Vikas Bhardwaj
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