The present work compares the two-alternative forced choice (2AFC) task to rating scales for measuring aesthetic perception of neural style transfer-generated images and investigates whether and to what extent the 2AFC task extracts clearer and more differentiated patterns of aesthetic preferences. To this aim, 8250 pairwise comparisons of 75 neural style transfer-generated images, varied in five parameter configurations, were measured by the 2AFC task and compared with rating scales. Statistical and qualitative results demonstrated higher precision of the 2AFC task over rating scales in detecting three different aesthetic preference patterns: (a) convergence (number of iterations), (b) an inverted U-shape (learning rate), and (c) a double peak (content-style ratio). Important for practitioners, finding such aesthetically optimal parameter configurations with the 2AFC task enables the reproducibility of aesthetic outcomes by the neural style transfer algorithm, which saves time and computational cost, and yields new insights about parameter-dependent aesthetic preferences.
|Number of pages||21|
|Journal||International Journal of Human-Computer Interaction|
|Publication status||Published - 2023|
Bibliographical noteFunding Information:
The author wants to express deepest gratitude to Prof. Karen Muckenhirn for proofreading subsequent versions of this manuscript, and to ML engineer Lars Sjoesund for feedback on the deep learning parts.
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Human-Computer Interaction
- Computer Science Applications