  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.
