A Deep Transfer Learning Based Visual Complexity Evaluation Approach to Mobile User Interfaces
Bibliographic record
Abstract
Visual complexity is an important factor affecting the efficiency and functionality of user interfaces. Its impact on the user's impression and the usability is significant, especially for mobile applications with constraints such as layout size, on screen keys and small input fields. Conventional approaches for visual complexity evaluation of user interfaces are either based on user evaluations with surveys or based on pre-specified formal metrics or on heuristics. Alternatively, in this study, we have explored the effectiveness of deep learning models for visual complexity evaluation, specifically, of mobile user interfaces. We have experimented with five state of the art pre-trained deep learning models known to be effective for computer vision tasks, namely, VGG16, DenseNet121, MobileNetv2, GoogleNet and ResNet152 were trained with 3635 different mobile user interface images as login, menu, search and settings. Furthermore, in order to validate the effectiveness of this approach, a new validation dataset and survey application was developed and an evaluation study was conducted with 98 participants where 7309 comparison result were obtained from the study. It was found that the agreement rate between the results of deep learning models and the user evaluations was up to 78% and 74% on the average. The high to moderate agreement rate between the results of deep learning models and the user evaluations reveals that this approach can be useful for designers in visual complexity evaluation of mobile user interfaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".