PiCCL: A lightweight multiview contrastive learning framework for image classification
Bibliographic record
Abstract
We introduce PiCCL (Primary Component Contrastive Learning), a self-supervised contrastive learning framework that utilizes a multiplex Siamese network structure consisting of many identical branches rather than 2 to maximize learning efficiency. PiCCL is simple and light weight, it does not use asymmetric networks, intricate pretext tasks, hard to compute loss functions or multimodal data, which are common for multiview contrastive learning frameworks and could hinder performance, simplicity, generalizability and explainability. PiCCL obtains multiple positive samples by applying the same image augmentation paradigm to the same image numerous times, the network loss is calculated using a custom designed Loss function named PiCLoss (Primary Component Loss) to take advantage of PiCCL's unique structure while keeping it computationally lightweight. To demonstrate its strength, we benchmarked PiCCL against various state-of-the-art self-supervised algorithms on multiple datasets including CIFAR-10, CIFAR-100, and STL-10. PiCCL achieved top performance in most of our tests, with top-1 accuracy of 94%, 72%, and 97% for the 3 datasets respectively. But where PiCCL excels is in the small batch learning scenarios. When testing on STL-10 using a batch size of 8, PiCCL still achieved 93% accuracy, outperforming the competition by about 3 percentage points.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".