SCNet: semi-supervised and contrastive learning against noisy labels with two selection strategy approach
Bibliographic record
Abstract
The deep learning-driven artificial intelligence diagnostic model heavily relies on high-quality and detailed annotated data during algorithm training. However, this process is susceptible to interference from noisy label information. To enhance the model's robustness and prevent the adverse effects of noisy labels, we propose a contrastive learning-based approach for noisy label image classification. Specifically, we designed three components: a mixed feature embedding module that takes mixed augmented images as input, a momentum update mechanism to explore abstract distributed feature representations, and two selection strategies. We incorporated a flexible pseudo-label boosting strategy, refining supervised information for noisy data with pseudo-labels based on initial classification predictions. By measuring the similarity between classification distributions, we effectively select more reliable pairs of confidence, thereby reducing the impact of noisy labels on contrastive learning. Furthermore, we employed a noise-robust loss function to ensure that samples with correct labels dominate the learning process. To validate the model's effectiveness, we conducted experiments on our collected data SAID (Sensitive Audio Information Detection), the CIFAR-10 and CIFAR-100 datasets, simulating scenarios with noisy label conditions.
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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 | 0.010 |
| 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 | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".