Quantitative evaluation of sensitivity in confidential car exterior design
Bibliographic record
Abstract
In recent years, the manufacturing industry has seen a shift in competition from performance, which can easily be evaluated numerically, to design which much more challenging to express numerically. The rise of companies that focus on design, such as Apple, Samsung and IKEA, is remarkable. However, design presents two challenges for the manufacturing industry. First, the sensory aspect of design is challenging to evaluate quantitatively, and unified evaluation indicators are not yet defined. Second, confidentiality of product design. In many cases, the design is kept in confidence within the companies, so it is often hesitated to investigate large customers. The above two problems increase the influence of the evaluator's experience and cause a situation that it is challenging to create a design desired by the customer. Therefore, the present study aims to enable inexpensive quantitative evaluation of automobile exterior design while maintaining confidentiality. We propose a technique that uses a convolutional neural network to link features extracted from accumulated design images to the sensitivity extracted from the customer's voice. This is then used to quantitatively evaluate an input image.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".