A Transformer Guided Generative Adversarial Network (TG-GAN) for Style Transfer in Artistic and Natural Scene Images
Bibliographic record
Abstract
The artistic style transfer is a technique to produce aesthetically pleasing images by merging the semantic content of one domain with the stylistic attributes of another.Most attentionbased GAN structures have been unable to achieve structural consistency and style fidelity across heterogeneous datasets.This paper presents a Transformer-Guided Generative Adversarial Network (TG-GAN) that incorporates multi-head self-attention into the generator to improve cross-domain feature alignment while ensuring perceptual realism.The role of the transformer is to flexibly align content-style relations using a novel adaptive token fusion approach, guided by a perceptual-adversarial optimization process.Results based on qualitative and quantitative evaluations on MS-COCO to WikiArt and MS-COCO to Flickr Landscapes demonstrate that TG-GAN achieves superior results over both StyTr² and DualStyleGAN in terms of structural integrity and stylization quality.The models proposed achieved an SSIM of 0.803, an FID of 24.5, and a Style Classification Accuracy of 91.3%, which is better than existing transformer-based GAN frameworks.The framework provides a promising pathway for scalable cross-domain and multimodal style transfer while also offering additional perspectives for integrating transformer architectures with generative adversarial learning.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".