Analysing Semi-Supervised ConvNet Model Performance with Computation Processes
Why this work is in the frame
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Bibliographic record
Abstract
The rapid development of semi-supervised machine learning (SSML) algorithms has shown enhanced versatility, but pinpointing the primary influencing factors remains a challenge. Historically, deep neural networks (DNNs) have been used to underpin these algorithms, resulting in improved classification precision. This study aims to delve into the performance determinants of SSML models by employing post-hoc explainable artificial intelligence (XAI) methods. By analyzing the components of well-established SSML algorithms and comparing them to newer counterparts, this work redefines semi-supervised computation processes for both data preprocessing and classification. Integrating different types of DNNs, we evaluated the effects of parameter adjustments during training across varied labeled and unlabeled data proportions. Our analysis of 45 experiments showed a notable 8% drop in training loss and a 6.75% enhancement in learning precision when using the Shake-Shake26 classifier with the RemixMatch SSML algorithm. Additionally, our findings suggest a strong positive relationship between the amount of labeled data and training duration, indicating that more labeled data leads to extended training periods, which further influences parameter adjustments in learning processes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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