Deep Learning for Neural Style Transfer

Konferenz: ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application
17.12.2021 - 19.12.2021 in Shenyang, China

Tagungsband: ICMLCA 2021

Seiten: 6Sprache: EnglischTyp: PDF

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Autoren:
Duan, Jiahui (School of Computer Science, University of Birmingham, UK)
Feng, Yajing (School of Information, Shanxi University of Finance and Economics, China)
Li, Zicheng (Faculty of Engineering, the University of Hong Kong, China)
Mo, Jianxing (Business College of Southwest University, China)

Inhalt:
The neural style transfer (NST) is an important task in the computer vision area. It aims to combine the content in an image with other images and generate an image with a specific style. This technique has been applied to many downstream image-related tasks and applications. Recently, the NST has made great progress with Convolution Neural Network (CNN), Generative Adversarial Network (GAN), and their variants. Thus, it is necessary to make a review of current work in NST. In this review, we firstly introduce the definition of NST and different types of NST algorithms. Then, we demonstrate some commonly used datasets and some indicators to evaluate the performance of different NST models. Finally, we analyze related work of NST and summarize some potential challenges and remained problems of state-of-the-art (SOTA) researches. This review will not only provide a good reference for researchers but also provide guidelines for some novices.