Approaches to Enhancing Semi-Supervised Learning using Process Data Augmentation via Self-Labeling and Generative Adversarial Networks
Bibliographic record
Abstract
Augmentation of limited data with additional new data has been of major importance in fields where available data is scarce or expensive to acquire, and this is achieved via either the generation of new synthetic data or the labelling of pre-existing unlabelled data. Data generation using generative adversarial networks (GANs) and their variants have been widely reported to produce useful synthetic data. Self-labeling of unlabelled data has also been successfully implemented to provide labels to augment the training set of limited datasets. In this work, we explore these approaches for improving the new data quality (either generated or self-labeled) for regression and classification problems using various experiments with a specific focus on froth flotation data. Our work proposes a combination of regressor and classifier networks with shared network layers both for generation of new data and self-labeling of unlabeled data. We show that this approach provides superior performance in comparison to just using a classifier network.
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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 | 0.002 |
| 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 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".