RAST: Restorable Arbitrary Style Transfer via Multi-restoration
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it