Exploring Preferential Label Smoothing for Neural Network-based Classifiers
Bibliographic record
Abstract
Overfitting, a common problem in Machine Learning, occurs when a predictive model learns the noise in the training data instead of the true underlying patterns and converges to perform very well with the training data but poorly with unseen data. Models that overfit cannot be deployed in practice. Regularization is a technique typically used to help a model better generalize. This is usually achieved by adding a penalty term in the loss function to discourage the model from fitting noise, making it more robust to noise in the data and, therefore more generalizable. One method of regularization is to take some of the concentration (called Smoothing Ratio (SR)) from the data sample’s ground truth label and distribute it uniformly among all the other labels during training. This method is called label smoothing and is a simple yet effective method to improve generalization. In this work, we explore what happens if we distribute the SR to the non-ground truth labels based on how closely they are related to the ground truth label, instead of uniformly. We call this approach of distributing the SR based on relation between labels as Preferential Label Smoothing (PLS). PLS represents a more unified approach of performing label smoothing. Ordinary uniform label smoothing becomes pointless as the number of labels becomes large since the SR proportion distributed per label becomes negligible. PLS is inconsequential in the case of binary classification, since there are only two labels. Therefore, we investigate the effects of PLS when the number of labels in the dataset is high. We also examine the effects of uniform and preferential label smoothing, as well as the absence of label smoothing, on the training dynamics. We demonstrate our study on image classification and text classification.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".