Learning From Crowds With Contrastive Representation
Bibliographic record
Abstract
Crowdsourcing provides a practical approach to obtaining annotated data for data-hungry deep models. Due to its simplicity and practicality, simultaneously learning the annotation correction mechanism and the target classifier is widely studied and applied. Existing work has improved performance from the annotator and annotation process modeling perspective. However, the instance representation, which most directly affects model training, has been neglected. In this work, we investigate contrastive representation to improve learning from crowds. Specifically, we first sample confident instances and positive pairs using the pre-trained representation and human annotations. Then, we extend the supervised contrastive loss to obtain a noise-tolerant version that supports continuous consistency between labels. After that, we leverage the learned representations to train the classifier and annotator parameters. The process is generally designed as an end-to-end framework, CrowdCons, compatible with existing crowdsourcing models. Our approach is evaluated on three real-world crowdsourcing datasets; LabelMe, CIFAR10-H and Music. The experimental results show that it can significantly improve prediction accuracy, and the case study demonstrates the robustness of the model regarding noisy annotations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".