Investigation the influence related to parameters configuration of Generative Adversarial Networks in face image generation
Bibliographic record
Abstract
Due to the excellent performance of Generative Adversarial Networks (GAN) for age regression on face images, it is particularly important to explore the effect of different parameters on model training. In this study, the origin and development of Artificial Intelligence (AI) is first discussed, from which the concept and principles of GAN are derived. This is followed by a brief introduction of the UTKface dataset used in this research, and the Conditional Adversarial Autoencoder (CAAE) framework based on the GAN technique. The division of labor and roles of the encoder, generator, and the two discriminators in the model are described. The various learning rates as well as batch size combinations attempted in this study are then illustrated, and the training results of the model are shown in the form of graphs and plots of the loss value function. A situation where the model stops learning is highlighted in the results, which is similar to pattern descent in GAN, and is shown to be characterized by the inability of the discriminator to successfully recognize it. Ultimately, drawing from the acquired outcomes, it can be deduced that employing a larger batch size serves to enhance the pace of model training. It is advisable to concurrently elevate the learning rate by an equivalent factor when augmenting the batch size, thereby ensuring a consistent trajectory for model convergence.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".