Impact of biased mislabeling on learning with deep networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of machine learning is to obtain a good model to correctly predict unseen data. In order to train such models, one needs a sufficient number of clean examples of the ground truth. However, some applications' datasets are not guaranteed to consist entirely of pure examples and might contain mislabeled data. Handling mislabeled data is a domain of outlier statistics and have been studied to some extent in the context of machine learning. Here we ask how does mislabeled data in a training set effect classification performance in deep neural networks. More specifically, motivated by an industrial application, we consider the case where the probability of the class mislabeling in the training set varies considerably between each class. We hence contrast in this paper the case of systematic mislabeling of one class to the more commonly studied situation of a uniform mislabeling between all classes. We demonstrate that the non-uniform mislabeling is more challenging than the more commonly studied uniform case. We also explicitly explore the dependence of our findings to the size of the training data which is not only a common limiting factor in industrial applications but which also has a large effect on the results. We demonstrate that deep networks have an inherent robustness when large datasets are available.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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