How Does a Neural Network's Architecture Impact Its Robustness to Noisy\n Labels?
Bibliographic record
Abstract
Noisy labels are inevitable in large real-world datasets. In this work, we\nexplore an area understudied by previous works -- how the network's\narchitecture impacts its robustness to noisy labels. We provide a formal\nframework connecting the robustness of a network to the alignments between its\narchitecture and target/noise functions. Our framework measures a network's\nrobustness via the predictive power in its representations -- the test\nperformance of a linear model trained on the learned representations using a\nsmall set of clean labels. We hypothesize that a network is more robust to\nnoisy labels if its architecture is more aligned with the target function than\nthe noise. To support our hypothesis, we provide both theoretical and empirical\nevidence across various neural network architectures and different domains. We\nalso find that when the network is well-aligned with the target function, its\npredictive power in representations could improve upon state-of-the-art (SOTA)\nnoisy-label-training methods in terms of test accuracy and even outperform\nsophisticated methods that use clean labels.\n
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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 | 0.022 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".