Imagine that you could transfer the style of Picasso into your own pieces of art. Your paintings would look exactly as if Pablo Picasso was actually the one creating them.
In fact, if presented side by side, they would be so indistinguishable that nobody could tell the difference.
Sounds pretty crazy, right?
Well, not if we are discussing Machine Learning.
Meet Neural Style Transfer.
Here’s what we’ll cover:
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Neural Style Transfer is the technique of blending style from one image into another image keeping its content intact. The only change is the style configurations of the image to give an artistic touch to your image.
The content image describes the layout or the sketch and Style being the painting or the colors. It is an application of Computer Vision related to image processing techniques and Deep Convolutional Neural Networks.
Neural Style Transfer deals with two sets of images: Content image and Style image.
This technique helps to recreate the content image in the style of the reference image. It uses Neural Networks to apply the artistic style from one image to another.
Neural style transfer opens up endless possibilities in design, content generation, and the development of creative tools.
Now, let’s explore how NST works.
The aim of Neural Style Transfer is to give the Deep Learning model the ability to differentiate between the style representations and content image.
NST employs a pre-trained Convolutional Neural Network with added loss functions to transfer style from one image to another and synthesize a newly generated image with the features we want to add.
Style transfer works by activating the neurons in a particular way, such that the output image and the content image should match particularly in the content, whereas the style image and the desired output image should match in texture, and capture the same style characteristics in the activation maps.
These two objectives are combined in a single loss formula, where we can control how much we care about style reconstruction and content reconstruction.
Here are the required inputs to the model for image style transfer:
Training a style transfer model requires two networks: a pre-trained feature extractor and a transfer network.
NST uses a pre-trained model trained on ImageNet- VGG in TensorFlow.
Images themselves make no sense to the model. These have to be converted into raw pixels and given to the model to transform it into a set of features, which is what Convolutional Neural Networks are responsible for.
Thus, somewhere in between the layers, where the image is fed into the model, and the layer, which gives the output, the model serves as a complex feature extractor. All we need to leverage from the model is its intermediate layers, and then use them to describe the content and style of the input images.
The input image is transformed into representations that have more information about the content of the image, rather than the detailed pixel value.
The features that we get from the higher levels of the model can be considered more related to the content of the image.
To obtain a representation of the style of a reference image, we use the correlation between different filter responses.
It helps to establish similarities between the content image and the generated image.
It is intuitive that higher layers of the model focus more on the features present in the image i.e. overall content of the image.
Content loss is calculated by Euclidean distance between the respective intermediate higher-level feature representation of input image (x) and content image (p) at layer l.
It is natural for a model to produce different feature maps in higher layers being activated in the presence of different objects.
This helps us to deduce that images having the same content should also have similar activations in the higher layers.
Style loss is conceptually different from Content loss.
We cannot just compare the intermediate features of the two images and get the style loss.
That's why we introduce a new term called Gram matrices.
Gram matrix is a way to interpret style information in an image as it shows the overall distribution of features in a given layer. It is measured as the amount of correlation present between features maps in a given layer.
Style loss is calculated by the distance between the gram matrices (or, in other terms, style representation) of the generated image and the style reference image.
The contribution of each layer in the style information is calculated by the below formula:
Thus, the total style loss across each layer is expressed as:
where the contribution of each layer in the style loss is depicted by some factor wl.
The architecture of the NST can be designed in such a way that it can range from applying a single style in an image to allowing mix and match of multiple styles.
Let’s have a look at the different possibilities.
Neural networks are employed to recreate the styled images in a single, feed-forward pass. For example, VGG16 models pre-trained on ImageNet are employed. Each model is small and compact both in terms of the model’s depth and size and is trained for a single style blend at a particular pass.
A single transmission network may create images in a variety of styles and merge them together.
Style transfer networks are fed a content image and style images with an additional vector, indicating how much of each style should be applied to the image.
This is a good technique to blend multiple styles and eliminates the overhead to train and store various models for different styles.
The above two models are limited to the use-case of producing images in styles that they've already seen during training.
Some random changes in style cannot be accommodated in the above models.
NST with an arbitrary style transfer model takes a content image and a style image and learns to extract and apply any variation of style to an image.
The stability of NST while training is very important, especially while blending style in a series of frames in a video.
The style should be constant in successive frames to avoid flickering effects in terms of color, contrast, and inconsistencies in terms of placement of objects. Two sequential frames are stylized and compared to each other such that the model learns to produce the same stylization for an object as it moves through the frame.
To ensure this an additional loss term for “temporal coherence” is added in the training of the model.
Additional attentional should be given to the perseverance of color of the content image while accounting for the stokes/style pigments from a reference image.
Finally, let's have a look at some of the real-world applications of the Neural Style Transfer.
Style transfer is extensively used in photo and video editing software.
These deep learning approaches and professional style transfer models can easily be applied to devices like mobile phones and give users a real-time ability to style images and videos.
Style transfer also provides new techniques that can change the way we look and deal with art.
It makes high-rated and over-priced artistic work reproducible for office and home decor, or for advertisements. Transfer models may help us commercialize art.
There are many cloud-powered video game streams that use image style transfer.
These models help the developers to provide interactive environments with customized artistic styles to users. This provides a 3D touch to the game and helps to enrich the artist inside every developer.
Much similar to gaming, VR apps help to tell visual stories through their applications, games, films, and more.
Let's do a quick recap of everything we've learned in this guide:
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