Evaluation in Neural Style Transfer: A Review

The field of neural style transfer (NST) has witnessed remarkable progress in the past few years, with approaches being able to synthesize artistic and photorealistic images and videos of exceptional quality. To evaluate such results, a diverse landscape of evaluation methods and metrics is used, in...

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Published in:	Computer graphics forum Vol. 43; no. 6
Main Authors:	Ioannou, Eleftherios, Maddock, Steve
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.09.2024
Subjects:	Algorithms image and video processing Image quality Performance evaluation rendering; non‐photorealistic rendering
ISSN:	0167-7055, 1467-8659
Online Access:	Get full text
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Abstract	The field of neural style transfer (NST) has witnessed remarkable progress in the past few years, with approaches being able to synthesize artistic and photorealistic images and videos of exceptional quality. To evaluate such results, a diverse landscape of evaluation methods and metrics is used, including authors' opinions based on side‐by‐side comparisons, human evaluation studies that quantify the subjective judgements of participants, and a multitude of quantitative computational metrics which objectively assess the different aspects of an algorithm's performance. However, there is no consensus regarding the most suitable and effective evaluation procedure that can guarantee the reliability of the results. In this review, we provide an in‐depth analysis of existing evaluation techniques, identify the inconsistencies and limitations of current evaluation methods, and give recommendations for standardized evaluation practices. We believe that the development of a robust evaluation framework will not only enable more meaningful and fairer comparisons among NST methods but will also enhance the comprehension and interpretation of research findings in the field. This review paper examines the different evaluation techniques in neural style transfer. It provides an in‐depth analysis of existing evaluation techniques and gives recommendations for standardized evaluation practices.
AbstractList	The field of neural style transfer (NST) has witnessed remarkable progress in the past few years, with approaches being able to synthesize artistic and photorealistic images and videos of exceptional quality. To evaluate such results, a diverse landscape of evaluation methods and metrics is used, including authors' opinions based on side‐by‐side comparisons, human evaluation studies that quantify the subjective judgements of participants, and a multitude of quantitative computational metrics which objectively assess the different aspects of an algorithm's performance. However, there is no consensus regarding the most suitable and effective evaluation procedure that can guarantee the reliability of the results. In this review, we provide an in‐depth analysis of existing evaluation techniques, identify the inconsistencies and limitations of current evaluation methods, and give recommendations for standardized evaluation practices. We believe that the development of a robust evaluation framework will not only enable more meaningful and fairer comparisons among NST methods but will also enhance the comprehension and interpretation of research findings in the field. The field of neural style transfer (NST) has witnessed remarkable progress in the past few years, with approaches being able to synthesize artistic and photorealistic images and videos of exceptional quality. To evaluate such results, a diverse landscape of evaluation methods and metrics is used, including authors' opinions based on side‐by‐side comparisons, human evaluation studies that quantify the subjective judgements of participants, and a multitude of quantitative computational metrics which objectively assess the different aspects of an algorithm's performance. However, there is no consensus regarding the most suitable and effective evaluation procedure that can guarantee the reliability of the results. In this review, we provide an in‐depth analysis of existing evaluation techniques, identify the inconsistencies and limitations of current evaluation methods, and give recommendations for standardized evaluation practices. We believe that the development of a robust evaluation framework will not only enable more meaningful and fairer comparisons among NST methods but will also enhance the comprehension and interpretation of research findings in the field. This review paper examines the different evaluation techniques in neural style transfer. It provides an in‐depth analysis of existing evaluation techniques and gives recommendations for standardized evaluation practices. The field of neural style transfer (NST) has witnessed remarkable progress in the past few years, with approaches being able to synthesize artistic and photorealistic images and videos of exceptional quality. To evaluate such results, a diverse landscape of evaluation methods and metrics is used, including authors' opinions based on side‐by‐side comparisons, human evaluation studies that quantify the subjective judgements of participants, and a multitude of quantitative computational metrics which objectively assess the different aspects of an algorithm's performance. However, there is no consensus regarding the most suitable and effective evaluation procedure that can guarantee the reliability of the results. In this review, we provide an in‐depth analysis of existing evaluation techniques, identify the inconsistencies and limitations of current evaluation methods, and give recommendations for standardized evaluation practices. We believe that the development of a robust evaluation framework will not only enable more meaningful and fairer comparisons among NST methods but will also enhance the comprehension and interpretation of research findings in the field.
Author	Ioannou, Eleftherios Maddock, Steve
Author_xml	– sequence: 1 givenname: Eleftherios orcidid: 0000-0003-3892-2492 surname: Ioannou fullname: Ioannou, Eleftherios email: eioannou1@sheffield.ac.uk organization: The University of Sheffield – sequence: 2 givenname: Steve orcidid: 0000-0003-3179-0263 surname: Maddock fullname: Maddock, Steve email: s.maddock@sheffield.ac.uk organization: The University of Sheffield
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Snippet	The field of neural style transfer (NST) has witnessed remarkable progress in the past few years, with approaches being able to synthesize artistic and...
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SubjectTerms	Algorithms image and video processing Image quality Performance evaluation rendering; non‐photorealistic rendering
Title	Evaluation in Neural Style Transfer: A Review
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