Papers › Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Or Litany
The success of learning with noisy labels (LNL) methods relies heavily on the success of a warm-up stage where standard supervised training is performed using the full (noisy) training set. In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels. We propose "Contrast to Divide" (C2D), a simple framework that solves this problem by pre-training the feature extractor in a self-supervised fashion. Using self-supervised pre-training boosts the performance of existing LNL approaches by drastically reducing the warm-up stage's susceptibility to noise level, shortening its duration, and improving extracted feature quality. C2D works out of the box with existing methods and demonstrates markedly improved performance, especially in the high noise regime, where we get a boost of more than 27% for CIFAR-100 with 90% noise over the previous state of the art. In real-life noise settings, C2D trained on mini-WebVision outperforms previous works both in WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an in-depth analysis of the framework, including investigating the performance of different pre-training approaches and estimating the effective upper bound of the LNL performance with semi-supervised learning. Code for reproducing our experiments is available at https://github.com/ContrastToDivide/C2D
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Code
Syntology Ran 6 of 7 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong; 2 ran · fixture could not drive it; 1 ran with no contract checked.
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Code Syntology ran Syntology
7 samples harvested; 6 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Harvested from ContrastToDivide/C2D. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 (with noisy labels) | C2D (ELR+ with SimCLR, ResNet-34) | Accuracy (under 20% Sym. label noise) | 96.74 ± 0.12 | #2 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (ELR+ with SimCLR, ResNet-34) | Accuracy (under 50% Sym. label noise) | 95.55 ± 0.32 | #2 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (ELR+ with SimCLR, ResNet-34) | Accuracy (under 80% Sym. label noise) | 93.11 ± 0.70 | #2 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (ELR+ with SimCLR, ResNet-34) | Accuracy (under 90% Sym. label noise) | 89.30 ± 0.21 | #2 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (ELR+ with SimCLR, ResNet-34) | Accuracy (under 95% Sym. label noise) | 80.21 ± 1.91 | #2 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (DivideMix with SimCLR, ResNet-18) | Accuracy (under 20% Sym. label noise) | 96.23 ± 0.09 | #4 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (DivideMix with SimCLR, ResNet-18) | Accuracy (under 50% Sym. label noise) | 95.15 ± 0.16 | #4 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (DivideMix with SimCLR, ResNet-18) | Accuracy (under 80% Sym. label noise) | 94.30 ± 0.12 | #4 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (DivideMix with SimCLR, ResNet-18) | Accuracy (under 90% Sym. label noise) | 93.42 ± 0.09 | #4 of 4 | Archive leaderboard | report |
| Image Classification | CIFAR-10 (with noisy labels) | C2D (DivideMix with SimCLR, ResNet-18) | Accuracy (under 95% Sym. label noise) | 87.72 ± 2.21 | #4 of 4 | Archive leaderboard | report |
| Image Classification | Clothing1M | ELR+ with C2D (ResNet-50) | Accuracy | 74.58 ± 0.15% | #17 of 51 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | DivideMix with C2D (ResNet-50) | ImageNet Top-1 Accuracy | 78.57 ± 0.37 | #14 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | DivideMix with C2D (ResNet-50) | ImageNet Top-5 Accuracy | 93.04 ± 0.10 | #14 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | DivideMix with C2D (ResNet-50) | Top-1 Accuracy | 79.42 ± 0.34 | #14 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | DivideMix with C2D (ResNet-50) | Top-5 Accuracy | 92.32 ± 0.33 | #14 of 47 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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