Separated and Independent Contrastive Semi-Supervised Learning for Imbalanced Datasets
Bibliographic record
Abstract
Conventional semi-supervised learning (SSL) encounters challenges in effectively addressing issues associated with long-tail datasets, primarily stemming from imbalances within a dataset. Existing contrastive SSL methods typically rely on pseudo-labels generated from unlabeled data, which can be inaccurate and introduce confirmation bias toward majority classes. To address this issue, we propose Separated and Independent Contrastive Semi-Supervised Learning (SICSSL), which applies supervised contrastive learning separately and independently to labeled and unlabeled samples. We validate SICSSL on four public benchmark datasets: CIFAR-10-LT, CIFAR-100-LT, STL-10-LT, and ImageNet-127. The results demonstrate consistent performance improvements over existing contrastive learning approaches. Furthermore, SICSSL is modular and easily integrates with recent SSL frameworks, including Auxiliary Balanced Classifier (ABC) and Adaptive Consistency Regularizer (ACR), enabling improved robustness in imbalanced semi-supervised settings. Source code is available at https://github.com/dongyyyyy/SICSSL.
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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 | 0.008 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".