ImCLR: Implicit Contrastive Learning for Image Classification
Bibliographic record
Abstract
Contrastive learning is an effective method for learning visual representations. In most cases, this involves adding an explicit loss function to encourage similar images to have similar representations, and different images to have different representations. In this paper, we introduce a clever construction for Implicit Contrastive Learning (ImCLR), primarily in the supervised setting: there, the network can implicitly learn to differentiate between similar and dissimilar images. Furthermore, this requires almost no change to existing pipelines, which allows for easy integration and for fair demonstration of effectiveness on a wide range of well-accepted benchmarks. Namely, there is no change to loss, no change to hyperparameters, and no change to general network architecture. We show that ImCLR improves the test error in the supervised setting across a variety of settings, including 3.24% on Tiny ImageNet, 1.30% on CIFAR-100, 0.14% on CIFAR-10, and 2.28% on STL-10. We show that this holds across different number of labeled samples, maintaining approximately a 2% gap in test accuracy down to using only 5\% of the whole dataset. We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0.72% improvement on CIFAR-100-C, and in the semi-supervised setting with a 2.16% improvement with the standard benchmark Π-model. We demonstrate that ImCLR is complementary to existing data augmentation techniques, achieving over 1% improvement on CIFAR-100 by combining ImCLR with CutMix over either baseline, and 2\% by combining ImCLR with AutoAugment over either baseline. Finally, we perform an ablation study.
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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 | 0.000 |
| 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 | 0.001 |
| 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".