Papers › To Smooth or Not? When Label Smoothing Meets Noisy Labels
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu
Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model. Later it was reported LS even helps with improving robustness when learning with noisy labels. However, we observed that the advantage of LS vanishes when we operate in a high label noise regime. Intuitively speaking, this is due to the increased entropy of ℙ(noisy label|X) when the noise rate is high, in which case, further applying LS tends to "over-smooth" the estimated posterior. We proceeded to discover that several learning-with-noisy-labels solutions in the literature instead relate more closely to negative/not label smoothing (NLS), which acts counter to LS and defines as using a negative weight to combine the hard and soft labels! We provide understandings for the properties of LS and NLS when learning with noisy labels. Among other established properties, we theoretically show NLS is considered more beneficial when the label noise rates are high. We provide extensive experimental results on multiple benchmarks to support our findings too. Code is publicly available at https://github.com/UCSC-REAL/negative-label-smoothing.
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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 | Clothing1M | Negative Label Smoothing (NLS) | Accuracy | 74.24% | #21 of 51 | Archive leaderboard | report |
| Learning with noisy labels | CIFAR-100N | Negative-LS | Accuracy (mean) | 58.59 | #13 of 24 | Archive leaderboard | report |
| Learning with noisy labels | CIFAR-10N-Aggregate | Negative-LS | Accuracy (mean) | 91.97 | #13 of 26 | Archive leaderboard | report |
| Learning with noisy labels | CIFAR-10N-Random1 | Negative-LS | Accuracy (mean) | 90.29 | #13 of 24 | Archive leaderboard | report |
| Learning with noisy labels | CIFAR-10N-Random2 | Negative-LS | Accuracy (mean) | 90.37 | #10 of 23 | Archive leaderboard | report |
| Learning with noisy labels | CIFAR-10N-Random3 | Negative-LS | Accuracy (mean) | 90.13 | #10 of 23 | 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.
Methods
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