Style Transferring with TensorFlow by@algoscale

Style Transferring with TensorFlow

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Algoscale

Algoscale is a Data consulting company covering data engineering, applied AI, data science, & product engineering.

With the latest development and research in artificial intelligence and machine learning, there are many frameworks available online, both paid and free. With the help of these platforms, different algorithms can be created and implemented.

While talking about different frameworks and platforms of machine learning one of the most prominent platforms is TensorFlow. TensorFlow is one of the best, free, and open-ended platforms for machine learning and artificial intelligence implementations.

The main aim of using TensorFlow is to train different models and to work on deep neural network models and their interference. The language used by TensorFlow is mostly JavaScript or Python. With the help of these languages, one can train different models of machine learning and artificial
intelligence. With the advancement in CNN as most of the machines, automobiles, industries have used CNN, machine learning, artificial intelligence, and deep learning. The researchers need a free and open-ended framework to test and train their models and for this purpose, TensorFlow is most widely used.

There are different applications and different purposes for using TensorFlow over conventional frameworks and platforms. Most of them are discussed in this article and their comparison is enlisted along with the figurative comparison. This article will provide a gateway for the new researchers in this field who want to pursue their careers in the field of machine learning or AI. (Introduction to TensorFlow, 2019)

In the previous paragraph, a brief discussion was made on TensorFlow. To continue further, there must have been a brief introduction to style transferring. Style transfer is basically a computer vision-based technique combined with image processing. In style transferring the composition of an image is changed in order to get different data sets for training purposes. It can be explained by a simple example.

Imagine you have a digital image and want to convert that picture painted by a famous artist the style transfer will convert this for us using different algorithms but having a prior dataset already uploaded to the platform by the developers. In style transferring there are two images as data set one is the image that has to be transferred and the other one is the image on the basis of image, the first image has to be changed. (Style Transfer Guide | Fritz AI, 2019)

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Figure 1. A common example of Style Transferring one is Content Image, the other is Style Image, and the result is Generated Image. (| Notebook.Community, 2019)

There are different methods to create such images and related algorithms. One of them is GAN (Neural Network - What’s the Relationship between Style Transfer and GAN? - Stack Overflow, 2019). It is a more generalized form but style transfer in TensorFlow keeps the image harmless and just paste the style image on the content image and provide the result as we have already observed in figure 1 shown above.

There is another technique adopted by researchers as well. The technique is a feed-forward neural network. With the help of this technique, they have provided much better and more accurate results. The main issue that occurs in the feed-forward technique is due to a large number of input images this technique requires a large computation speed and a large time is required. (Ma et al., 2020)

In style transferring in tensor flow, we can observe that the content image can adapt the colors obtained from the reference image. In this way, we can get a single image having the features of two images. In tensor flow, there are different layers in the content and style images. In the input image, there are some low features like edges and features. There are certain other layers which are final layers which are high-level features like wheels and eyes and in order to get extracted such features a lot of computation power is required but in with the help of TensorFlow such computation speed can be achieved and a lot of features can be extracted from the reference image as well. (Neural Style Transfer  |  TensorFlow Core, 2019)

The technique is pretty important while talking about convolutional neural networks (How Do Neural Style Transfers Work? | by Blackburn | Towards Data Science, 2020). As they have combined the features of one image and the color of the other. This can be helpful to develop AI-based software which is mostly related to image processing, image editing, and video editing. By training proper data, setting up a proper database, and having the system with proper computational speed. Even at present, Facebook, Twitter, YouTube, and face unlocking software use this technique in order to get better results.

The most common type of style transfer is Text style transfer or (TST) which is very much important in Natural Language
Programming (NLP). In TST, the different expressions like humor, anger, and excitement can be translated into subtitles in movies. The autogenerated captions on Facebook and YouTube videos are based on the TST technique. As this technique is new that is the reason the results are not accurate or up to the mark. (Two Minutes NLP — Quick Intro to Text Style Transfer | by Fabio Chiusano | NLPlanet | Medium, 2019)

There are many uses of style transferring. The most common of them are enlisted as follows. (Gatys et al., 2016)

i. With the help of this technique, one can create
artwork from the natural image with the help of artificial intelligence.

ii. There are many notable mobile applications like Prisma, and DeepArt which have used this technique to develop such applications.

iii. There are many techniques in computer science in which style transferring is used. The most common of them is object detection.

iv. In supervised and unsupervised learning of object detection where TST and fast-feed style transferring has been used.

v. This technique has been widely used in image processing techniques like image stylization and image analogies.

References

| notebook.community. (n.d.). Retrieved February 19, 2022, from https://notebook.community/diegocavalca/Studies/books/deep-learning-with-python/8.3-neural-style-transfer

Gatys, L., Ecker, A., & Bethge, M. (2016). A Neural Algorithm of Artistic Style. Journal of Vision, 16(12), 326. https://doi.org/10.1167/16.12.326

How Do Neural Style Transfers Work? | by blackburn | Towards Data Science. (n.d.). Retrieved February 19, 2022, from https://towardsdatascience.com/how-do-neural-style-transfers-work-b76de101eb3

Introduction to TensorFlow. (n.d.). Retrieved February 19, 2022, from https://www.tensorflow.org/learn

Ma, W., Chen, Z., & Ji, C. (2020). Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited Memory. IEEE Access, 8, 158056–158066. https://doi.org/10.1109/ACCESS.2020.3020053

neural network - What’s the relationship between Style Transfer and GAN? - Stack Overflow. (n.d.). Retrieved February 19, 2022, from https://stackoverflow.com/questions/55099376/whats-the-relationship-between-style-transfer-and-gan

Neural style transfer  |  TensorFlow Core. (n.d.). Retrieved February 19, 2022, from https://www.tensorflow.org/tutorials/generative/style_transfer

Style Transfer Guide | Fritz AI. (n.d.). Retrieved February 19, 2022, from
https://www.fritz.ai/style-transfer/

Two minutes NLP — Quick intro to Text Style Transfer | by Fabio Chiusano | NLPlanet | Medium. (n.d.). Retrieved February 19, 2022, from https://medium.com/nlplanet/two-minutes-nlp-quick-intro-to-text-style-transfer-61de9cbd4083

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