A study on optimizing deep learning models for creative generation of animated new media advertisements: an application based on improved generative adversarial networks (GANs) and variational autocoders (VAEs)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to optimize the performance of generative adversarial networks on automatic advertisement image generation, this paper combines the variational self-encoder with generative adversarial networks, which consists of four parts: encoder network, decoder network, target-to-be-attacked network, and discriminator network to form a new adversarial sample generation method based on GANs, i.e., AdvAE-GAN model.To make the generated samples more clear and natural, the adversarial learning mechanism and similarity metric (PCE) are added to the AdvAE-GAN model.To obtain the performance of the model in diverse image coloring, multiple methods are elicited for subjective and objective qualitative evaluation and model complexity analysis, respectively.Combining the four standard datasets of AWA, CUB, SUN and FLO, zero-sample image recognition, generalized zero-sample learning experiments are carried out sequentially to derive the loss value curve of the model.The visual effects of animated advertisements generated by AdvAE-GAN model are rated using questionnaire research.For the product effect of animated advertisements generated by AdvAE-GAN model, the category diversity, design diversity, animation contour completeness, and image clarity indexes with scores above 7 account for 70.47%, 85.82%, 76.73%, and 84.02%, respectively.The animated advertisement generation model based on improved generative adversarial network is recognized by the market as well as the society and can be deepened.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it