Image Style Conversion Optimization Method in Animation Design Based on Deep Convolutional Neural Networks
Bibliographic record
Abstract
With the continuous development and popularization of the animation field, animation style products have received more and more attention, and the traditional style migration algorithm can no longer meet people's needs.In this paper, after researching the style principle of convolutional neural network and generative adversarial network, we propose an improved image style conversion algorithm based on convolutional neural network, introducing depth-separable convolution and cross-layer connection method to realize the information fusion of low-layer convolution and highlayer convolution, and at the same time, combining four different functions for the fitting of loss function.The proposed method in this paper is trained and validated on Facades dataset, Monet2photo dataset, Summer2winter dataset and Apple2orange dataset, and the quantitative experimental results show that the MOS value of the proposed method in this paper is improved on average compared with CycleGAN and StarGAN by about 12.67% and 52.93%, thus it can be concluded that the method proposed in this paper effectively improves the overall texture details and style quality of image style transformation in animation design.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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 teacher head, 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".