Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of arbitrary style transfer is to apply a given artistic or photo-realistic style to a target image. Although current methods have shown some success in transferring style, arbitrary style transfer still has several issues, including content leakage. Embedding an artistic style can result in unintended changes to the image content. This article proposes an iterative framework called Restorable Arbitrary Style Transfer (RAST) to effectively ensure content preservation and mitigate potential alterations to the content information. RAST can transmit both content and style information through multi-restorations and balance the content-style tradeoff in stylized images using the image restoration accuracy. To ensure RAST’s effectiveness, we introduce two novel loss functions: multi-restoration loss and style difference loss. We also propose a new quantitative evaluation method to assess content preservation and style embedding performance. Experimental results show that RAST outperforms state-of-the-art methods in generating stylized images that preserve content and embed style accurately.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it