Sampling Informative Positives Pairs in Contrastive Learning
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Bibliographic record
Abstract
Contrastive Learning is a paradigm for learning representation functions that recover useful similarity structure in a dataset based on samples of positive (similar) and negative (dissimilar) instances. The quality of the learned representations depends crucially on the degree to which the strategies for sampling positive and negative instances reflect useful structure in the data. Typically, positive instances are sampled by randomly perturbing an anchor point using some form of data augmentation. However, not all randomly sampled positive instances are equally effective. In this paper, we analyze strategies for sampling more effective positive instances. We consider a setting where class structure in the observed data derives from analogous structure in an unobserved latent space. We propose active sampling approaches for positive instances and investigate their role in effectively learning representation functions which recover the class structure in the underlying latent space.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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