Application and Evaluation of Artificial Intelligence in Sustainable Fashion Design
Bibliographic record
Abstract
The development of Artificial Intelligence has renewed the direction of art history, making the relationship between technology and art a matter of great interest once again.The application of artificial intelligence in the field of fashion design brings new tools to the designers' way of designing and displaying.This paper researches artificial intelligence technology and analyzes the application of artificial intelligence as an auxiliary means in the field of art and design, and deeply researches the way of applying artificial intelligence in fashion design as well as its advantages.It also researches the intelligent image generation problem under the fashion big data environment, adopts the method of fusing the external features of fashion images and decoupling the internal features, and provides theoretical methods and bases for the controllable generation of fashion images based on the architecture of generative adversarial network.A multiconditional information fusion generative adversarial network architecture (MCF-GAN) is proposed, and the experimental results show that the image generation performance of the model in this paper is excellent, and better performance is obtained compared with other comparative methods.And it is applied to the actual fashion design for evaluation, the designer's evaluation in all dimensions are more than 10 points, indicating that the method in this paper has a better application value in fashion design, and provides an effective path for fashion design optimization.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".