Design of a neural network-based automated style migration technique in digital media art
Bibliographic record
Abstract
The continuous development of neural network makes the automated style migration technology also rise to a new height.This paper selects digital media art as the research field, constructs Cycle GAN, a cyclic consistent generative adversarial network structure applied to digital media art, on the basic framework of GAN, and optimizes it by adding bilinear interpolation and attention mechanism, so as to build up a style migration model for digital media art.In the style migration simulation experiment, the IS test values of this paper's model on the photo2vangogh and photo2monet datasets are 5.32 and 6.03, and the FID test values are 97.52 and 75.55, which are better than the other comparative models.Similarly, the optimized performance of FID, SSIM and PSNR values on the dataset is also better than other comparative models, and the style migration performance of the model is verified.Using the model of this paper to design a digital topography with Chinese traditional ink painting as the content, we explore the correlation between the design attributes of the style migration design works in digital media art and the audience's cognitive evaluation and overall perception.Among the design attributes, "plot relevance" (4.375) and "atmosphere rendering" (3.38) have the highest T-value, which is the most important influence on audience perception.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".