  Noisy labels are inevitable in large real-world datasets. In this work, we
explore an area understudied by previous works -- how the network's
architecture impacts its robustness to noisy labels. We provide a formal
framework connecting the robustness of a network to the alignments between its
architecture and target/noise functions. Our framework measures a network's
robustness via the predictive power in its representations -- the test
performance of a linear model trained on the learned representations using a
small set of clean labels. We hypothesize that a network is more robust to
noisy labels if its architecture is more aligned with the target function than
the noise. To support our hypothesis, we provide both theoretical and empirical
evidence across various neural network architectures and different domains. We
also find that when the network is well-aligned with the target function, its
predictive power in representations could improve upon state-of-the-art (SOTA)
noisy-label-training methods in terms of test accuracy and even outperform
sophisticated methods that use clean labels.
