Improved Artistic Images Generation Using Transfer Learning
Bibliographic record
Abstract
The existing methods for photographic image generation have defects in both content preservation and style transform, which suppress the accuracy of the generated images. This paper attempts to improve the quality of photographic images generated based on inputted content images and style images. The transfer learning with VGG-19 model, a convolutional neural network (CNN), was adopted to extract the features from inputted style image and apply them to the content image. Then, a loss function was defined based on the ImageNet model, and used to capture the difference between the images generated based on the content image and the style image. In addition, the VGG-19 model was trained on a very large ImageNet database, aiming to improve its ability to identify image features of any dimension. Finally, several experiments were conducted to compare our method and several existing methods. The results show that the photographic images generated by our image retain the features of inputted content and style images, and minimizes the discrepancy between content and style.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".