Summary of Evaluation in Neural Style Transfer: a Review, by Eleftherios Ioannou and Steve Maddock
Evaluation in Neural Style Transfer: A Review
by Eleftherios Ioannou, Steve Maddock
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This review paper delves into the assessment methods used to evaluate Neural Style Transfer (NST) models, which have achieved impressive results in generating artistic and photorealistic images and videos. While various evaluation metrics are employed, including human judgment studies and computational measurements, there is no consensus on a reliable evaluation procedure. The authors provide an in-depth examination of existing techniques, highlighting inconsistencies and limitations, and propose standardized practices to facilitate fairer comparisons among NST methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we measure the quality of pictures made using Neural Style Transfer (NST). People have developed many ways to do this, like asking people what they think or using computers to calculate scores. But nobody agrees on which method is best. The authors of this review examine these different methods and find that some are better than others. They suggest we should all use the same methods so we can compare pictures more fairly. |
Keywords
* Artificial intelligence * Style transfer
