RAST: Restorable Arbitrary Style Transfer via Multi-restoration
Bibliographic record
Abstract
Arbitrary style transfer aims to reproduce the target image with the artistic or photo-realistic styles provided. Even though existing approaches can successfully transfer style information, arbitrary style transfer still faces many challenges, such as the content leak issue. Specifically, the embedding of artistic style can lead to content changes. In this paper, we solve the content leak problem from the perspective of image restoration. In particular, an iterative architecture is proposed to achieve the Restorable Arbitrary Style Transfer (RAST), which can realize transmission of both content and style information through multi-restorations. We control the content-style balance in stylized images by the accuracy of image restoration. In order to ensure effectiveness of the proposed RAST architecture, we design two novel loss functions: multi-restoration loss and style difference loss. In addition, we propose a new quantitative evaluation method to measure content preservation performance and style embedding performance. Comprehensive experiments comparing with state-of-the-art methods demonstrate that our proposed architecture can produce stylized images with superior performance on content preservation and style embedding.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".