KBStyle: Fast Style Transfer Using a 200 KB Network With Symmetric Knowledge Distillation
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) have achieved remarkable progress in arbitrary artistic style transfer. However, the model size of existing state-of-the-art (SOTA) style transfer algorithms is immense, leading to enormous computational costs and memory demand. It makes real-time and high resolution hard for GPUs with limited memory and limits the application on mobile devices. This paper proposes a novel arbitrary artistic style transfer algorithm, KBStyle, whose model size is only 200 KB. Firstly, we design a style transfer network where the style encoder, content encoder, and corresponding decoder are custom designed to guarantee low computational cost and high shape retention. Besides, the weighted style loss function is presented to improve the performance of style migration. Then, we propose a novel knowledge distillation method (Symmetric Knowledge Distillation, SKD) for encoder-decoder-based style transfer models, which redefines the knowledge and symmetrically compresses the encoder and decoder. With the SKD, the proposed style transfer network is further compressed by 14 times to achieve the KBStyle. Experimental results demonstrate that the proposed SKD method achieves comparable results with other SOTA knowledge distillation algorithms for style transfer. Besides, the proposed KBStyle achieves high-quality stylized images. And the inference time of the KBStyle on an Nvidia TITAN RTX GPU is only 20 ms when the resolutions of the content image and style image are both 2k-resolution ( 2048×1080 ). Moreover, the 200 KB model size of KBStyle is much smaller than the SOTA models and facilitates style transfer on mobile devices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".