神经风格迁移中的评估：综述
Evaluation in Neural Style Transfer: A Review
AI总结：
本文综述了神经风格迁移的评估方法，分析了现有评估技术的不一致性与局限，并提出标准化评估实践建议，以促进更公平的方法比较和更可靠的研究结果解读。
摘要：
神经风格迁移（NST）领域在过去几年中取得了显著进展，相关方法能够合成具有卓越质量的艺术化和照片级真实感的图像与视频。为了评估这些结果，研究者使用了多样化的评估方法和指标，包括基于并排比较的作者主观意见、量化参与者主观判断的人类评估研究，以及大量客观评估算法性能不同方面的定量计算指标。然而，关于最合适且有效的评估程序，目前尚未达成共识，无法保证结果的可靠性。在本综述中，我们对现有评估技术进行了深入分析，指出了当前评估方法的不一致性和局限性，并提出了标准化评估实践的建议。我们相信，一个稳健评估框架的发展不仅能够使NST方法之间进行更有意义和更公平的比较，还将增强对该领域研究结果的理解和解释。
英文摘要：
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.
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