Papers › Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Lu Jiang, Di Huang, Mason Liu, Weilong Yang
Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting. This paper makes three contributions. First, we establish the first benchmark of controlled real-world label noise from the web. This new benchmark enables us to study the web label noise in a controlled setting for the first time. The second contribution is a simple but effective method to overcome both synthetic and real noisy labels. We show that our method achieves the best result on our dataset as well as on two public benchmarks (CIFAR and WebVision). Third, we conduct the largest study by far into understanding deep neural networks trained on noisy labels across different noise levels, noise types, network architectures, and training settings. The data and code are released at the following link: http://www.lujiang.info/cnlw.html
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | WebVision-1000 | MentorMix (InceptionResNet-V2) | ImageNet Top-1 Accuracy | 67.5% | #12 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MentorMix (InceptionResNet-V2) | ImageNet Top-5 Accuracy | 87.2% | #12 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MentorMix (InceptionResNet-V2) | Top-1 Accuracy | 74.3% | #12 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | MentorMix (InceptionResNet-V2) | Top-5 Accuracy | 90.5% | #12 of 16 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | MentorMix (Inception-ResNet-v2) | ImageNet Top-1 Accuracy | 72.9 | #35 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | MentorMix (Inception-ResNet-v2) | ImageNet Top-5 Accuracy | 91.1 | #35 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | MentorMix (Inception-ResNet-v2) | Top-1 Accuracy | 76.0 | #35 of 47 | Archive leaderboard | report |
| Image Classification | mini WebVision 1.0 | MentorMix (Inception-ResNet-v2) | Top-5 Accuracy | 90.2 | #35 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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