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As deep neural networks can easily overfit noisy labels, robust training inthe presence of noisy labels is becoming an important challenge in modern deeplearning. While existing methods address this problem in various directions,they still produce unpredictable sub-optimal results since they rely on theposterior information estimated by the feature extractor corrupted by noisylabels. Lipschitz regularization successfully alleviates this problem bytraining a robust feature extractor, but it requires longer training time andexpensive computations. Motivated by this, we propose a simple yet effectivemethod, called ALASCA, which efficiently provides a robust feature extractorunder label noise. ALASCA integrates two key ingredients: (1) adaptive labelsmoothing based on our theoretical analysis that label smoothing implicitlyinduces Lipschitz regularization, and (2) auxiliary classifiers that enablepractical application of intermediate Lipschitz regularization with negligiblecomputations. We conduct wide-ranging experiments for ALASCA and combine ourproposed method with previous noise-robust methods on several synthetic andreal-world datasets. Experimental results show that our framework consistentlyimproves the robustness of feature extractors and the performance of existingbaselines with efficiency. Our code is available athttps://github.com/jongwooko/ALASCA.

A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise