51311013e51adebc3c34d2cc591fefee-Supplemental.pdf

Appendix: How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? In this section, we include additional experimental results for the predictive power in (a) representations from randomly initialized models (Appendix A.1), (b) representations learned under different We first evaluate the predictive power of randomly initialized models (a.k.a., untrained models), and Notice that lower test MAPE denotes better test performance.Model T est MAPE Random Trained Max-sum GNN 12.74 0.57 0.37 0.08 In previous experiments (section 4.2), we have shown that the predictive power in well-aligned MAE, is more helpful in learning more predictive representations under smaller noise ratios. The predictive in the representations grows as the mutual information between the noisy labels and original clean labels increases for models well-aligned with the target function. Clean Labels (DwC) and further measure the predictive power in representations learned by DwC. Table 6: Test accuracy (%) on CIF AR-10 with flipped label noise .