Transfer learning strategies for neural networks: A case study in amine gas treating units
Bibliographic record
Abstract
This work presents a framework where strategies are applied within a workflow created to enhance the accuracy and transferability of the transfer learning process. We used a case study for predicting corrosion rates in gas-treating units, employing datasets from two different amines (A and B), where the amine A dataset is large compared to the amine B dataset. In the first neural network-based strategy, the inlet to hidden layer weights and biases are frozen after being trained with dataset A, while the ones from the hidden layer to the end response are freed. The freed weights and biases are then estimated via optimization. A set of X% of the amine B dataset values is added to the amine dataset A, and the neural network is then refitted, showing an increment in accuracy as more data from amine B is added. In the second neural network-based strategy, fine-tuning was used through a loss cross-entropy function in addition to' freezing' layers. Moreover, we compared our approach against Transfer-LASSO, an approach based on LASSO regression that looks at reducing model complexity while adding sparsity via regularization. Metrics of accuracy and transferability are introduced to evaluate class imbalance, computational time, and the effect of outliers. Our findings serve as a set of considerations when selecting transfer learning approaches in engineering problems. • Framework/workflow to enhance accuracy and transferability of the transfer learning process. • A comparison analysis between neural network-based transfer learning and Transfer-LASSO was performed. • Findings serve as a set of considerations when selecting transfer learning approaches in engineering.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".