Meta Generative Image and Text Data Augmentation Optimization
Bibliographic record
Abstract
Abstract This paper proposes a method called Meta Generative Data Augmentation Optimization (MGDAO) to overcome limited types of operations for the policy-based automatic data augmentation method. While traditional data augmentation methods have relied on expert intuition to determine effective transformations, recent approaches have attempted to generate data augmentation strategies automatically. However, these automatic methods can still suffer from limited operation sets and difficulty training conditional generative models. To address these issues, MGDAO replaces the limited operations space in the AutoAugment series with a deep-style generator and replaces the discriminator in a generative adversarial model with the validation loss of the target model. These replacements released fixed image operations and made MGDAO useful for sequential data. The generator learns to transform the data from the training domain to the validation data domain. It is further used to generate augmented samples to train the target model and reduce the validation loss. Experiments on classification benchmarks of few-shot image and text-based datasets show that MGDAO achieves competitive results compared to data auto-augmentation methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".