CTIP: Towards Accurate Tabular-to-Image Generation for Tire Footprint Generation
Bibliographic record
Abstract
Generating images directly from tabular data while ensuring an accurate representation of ground truth is a useful application in manufacturing. Simply embedding tabular data to use it as a condition in image generation models often fails to learn the correspondence between tabular features and their impact on the generated image. To overcome this limitation, we propose Contrastive Tabular-Image Pre-training (CTIP), inspired by the CLIP framework. These pre-train methods help improve the quality of the embedding of the tabular encoder on the tabular data, which then helps improve the performance of the image generation model. CTIP uses contrastive learning on multiple tabular and image data pairs, allowing the model to learn how changes in certain tabular features affect images. This approach is particularly crucial in manufacturing, where accurate capture of product outcomes under varying conditions is essential. We demonstrate that applying CTIP enhances image generation performance, yielding images that closely match ground truth images, even in Feature Few-shot or Feature Zero-shot scenarios where specific features are sparse or novel. We further show the application of CTIP in tire development, where tire footprint images are generated based on tire specifications and test conditions. CTIP produces high-quality embeddings that align well with ground truth images and effectively handle the scarcity or sparseness of specific features, addressing common challenges in new product development. Our code is available in https://github.com/Noverse0/CTIP.git.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".