To Smooth or Not? When Label Smoothing Meets Noisy Labels
This work addresses the problem of robust model training with noisy labels for machine learning practitioners, offering insights into when to use or avoid smoothing techniques.
The paper investigates the effectiveness of label smoothing (LS) and introduces negative label smoothing (NLS) for learning with noisy labels, finding that LS loses its advantage in high-noise regimes while NLS provides benefits, supported by theoretical analysis and experiments on benchmarks.
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 $\mathbb{P}(\text{noisy label}|X)$ 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.