A Appendix

A.1 Data, Models, and Model Accuracies Images are pixel-wise normalized by the mean and standard deviation of the training images for each dataset, and for ImageNet all images are center cropped and resized to 224 224; this preprocessing is done before any interpolating paths are constructed. Our implementations are based on Cubuk et al. [6]; we use the same optimizer (stochastic gradient descent with momentum) and cosine learning rate schedule. We train without data augmentation (to ensure all models are trained on exactly the same examples), except for experiments that explicitly vary data augmentation. Without data augmentation, the test accuracies of our models are shown in Table 1. The top-1 classification accuracies of these models are presented in Table 2. Model ImageNet Test Accuracy (%) A.2 Linear Interpolation: Methodological Details For each sampled path, we compute the discrete Fourier transform (DFT) of the prediction function along the path separately for each of the M class predictions, take the (real) magnitude of the resulting (complex) DFT coefficients, and average them among the M classes.