Supplement: Robustness to Label Noise Depends on the Shape of the Noise Distribution

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] Instructions are We will provide code after internal review for release. Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? Did you include the total amount of compute and the type of resources used (e.g., type Did you include any new assets either in the supplemental material or as a URL? [N/A] Did you discuss whether and how consent was obtained from people whose data you're If you used crowdsourcing or conducted research with human subjects... (a) Proofs provided for theoretical results of Section 3. A.1 Uniform noise Proof. Lemma 3.2: Let c, ϵ, m (c 1) ( c 1) ( c 1) (c 1) (c 1) (c 1) (c 1) (c 1) ( c 1) Lemma 3.5: Let c, ϵ, m The proof of Theorem 3.6 is identical to that of Theorem 3.3 except using the value of Fig. S3 shows the same results as Fig. S3, but with the accuracy results of the vanilla (no label noise Fig. S4 compares the clean test accuracy on 10-class, 5-dimensional synthetic data of two label-noise Each of the methods is run with default parameters found in the corresponding repositories. All of our experiments utilize the ResNet-32 architecture across all mitigation methods.