Neural Style Transfer

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Neural Style Transfer

What is Neural Style Transfer?

Neural style transfer is an optimization technique that involves taking two images, a content image and a style reference image, and blending them. It builds on the idea that you could separate style representations and content representations in a convolutional neural network learned while performing a computer vision task like an image recognition task.

In neural style transfer, a pre-trained convolution neural network (CNN) is used to transfer styles from one image to another. To do this, a loss function is defined which will attempt to minimize the differences between a content image, a style reference image, and a generated image. 

The technique is essentially an example of image stylization, a problem that has been studied for upwards of twenty years in the field of non-photorealistic rendering. 

Neural style transfer was initially published in the paper "A Neural Algorithm of Artistic Style" by Leon Gatys et al. in 2015. It was later accepted by the peer-reviewed journal of Computer Vision and Pattern Recognition in 2016.

It is based on histogram-based texture synthesis algorithms, especially the method of Portilla and Simoncelli. To summarize neural style transfer, you could essentially call it a histogram-based text synthesis that has convolutional neural network features for the image analogies problem.

Neural style transfer has even been extended to videos now.

Fei-Fei Li et al. made use of a different regularized loss metric and managed to accelerate the method for training to allow it to generate results in real-time, making it thrice as fast as Leon Gatys’s. Instead of using a pixel-based loss, they made use of a perpetual loss measuring the differences between higher-level layers within the CNN. They employed a symmetric encoder-decoder convolutional neural network. The training utilized a loss function that was similar to the basic NST but even regularizes the output for smoothness by using a total loss function. After the network is trained, it can be used to transform an image using the style that was used during the training process by making use of a single feed-forward pass of the network. This network is restricted to the single style on which it has been trained.

Google AI even created a method in 2017 that made it possible for a single deep convolutional style transfer network to learn multiple styles simultaneously. This Google AI algorithm even allows you to perform style interpolation in real-time, even when it is carried out on video media.

Neural Style Transfer
Source: Medium

Is neural style transfer important?

Deep learning neural networks have reached the point where they perform far better than humans in tasks like object recognition and object detection. But, until a few years ago, deep learning networks were far behind when it came to tasks like generating artistic artefacts having high perceptual quality.

Creating higher quality art by making use of machine learning techniques is critical for deep learning to reach human-like capabilities. It even opens up a wide new range of possibilities.

With the progress made in computer hardware and the spread of deep learning, deep learning is now being used for the purpose of creating art.

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What is the use of neural style transfer?

Neural style transfer is used in photo editing. It goes beyond your typical Instagram filters where transformations are only applied in the color space. The transformations in neural style transformation are far more sophisticated and elaborate. They aren’t just limited to color.

For example, a few months after Leon Gatys’s paper on neural style transfer came out, a mobile app called Prisma was launched. This app made use of neural style transfer and allowed users to apply filters to their images that weren’t limited to color. It actually allowed users to apply the painting styles of famous painters to their own photographs.

Neural style transfer could also find applications in data augmentation. It is even possible to use neural style transfer to change interfaces. For example, ImagineNet uses a neural style transfer model that makes it possible to use an artwork as a style reference and change the visual appearance interface of a mobile application.

Is neural style transfer supervised learning?

Neural style transfer can’t be considered to be supervised learning. In fact, it isn’t unsupervised learning either. To be honest, neural style transfer isn’t really machine learning at all. It’s actually a really cool side effect or output of machine learning on image tasks.

When you perform neural style transfer using a pre-trained CNN, a whole lot of supervised machine learning has already been performed to make it possible. 

The neural style transfer algorithm is an example of gradient-based cost function optimization that it shares with several supervised as well as unsupervised machine learning algorithms.

The result of neural style transfer is partially a probe into the learnings of the network about different levels of structure in the problem domain that it was trained on.

So neural style transfer is part of a convolution neural network (CNN), but it is not machine learning. It definitely is not supervised learning because you aren’t focusing on any predefined output, we’re just getting a modified version of the images.

What is content loss in neural style transfer?

Content loss makes sure that the content that you want in the generated image is captured efficiently. Convolution neural networks capture information about the content in the higher levels of the network, while the lower levels tend to focus more on the individual pixel values.

It is based on the intuition that images that have similar content will also have similar representation in the higher layers of the network.

What is style loss in neural style transfer?

Style essentially involves capturing the brush strokes and patterns. For this purpose, the lower layers are primarily used because they capture lower-level features.

It takes more effort to define the loss function for style than for content because multiple layers are involved in computing. We measure the style information as the amount of correlation present between the feature maps per layer. 

The Gram Matrix is used for the purpose of computing style loss.

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