  As deep neural networks can easily overfit noisy labels, robust training in
the presence of noisy labels is becoming an important challenge in modern deep
learning. While existing methods address this problem in various directions,
they still produce unpredictable sub-optimal results since they rely on the
posterior information estimated by the feature extractor corrupted by noisy
labels. Lipschitz regularization successfully alleviates this problem by
training a robust feature extractor, but it requires longer training time and
expensive computations. Motivated by this, we propose a simple yet effective
method, called ALASCA, which efficiently provides a robust feature extractor
under label noise. ALASCA integrates two key ingredients: (1) adaptive label
smoothing based on our theoretical analysis that label smoothing implicitly
induces Lipschitz regularization, and (2) auxiliary classifiers that enable
practical application of intermediate Lipschitz regularization with negligible
computations. We conduct wide-ranging experiments for ALASCA and combine our
proposed method with previous noise-robust methods on several synthetic and
real-world datasets. Experimental results show that our framework consistently
improves the robustness of feature extractors and the performance of existing
baselines with efficiency. Our code is available at
https://github.com/jongwooko/ALASCA.
