Title: ESP: Exponential Smoothing on Perturbations for Increasing Robustness to Data Corruptions. Abstract: Despite the great advances in the machine learning field over the past decade, deep learning algorithms are often vulnerable to data corruption in real-world environments. We propose a simple yet efficient data augmentation method named Exponential Smoothing on Perturbations (ESP) that imposes perturbations on training data to enhance a model’s robustness to unforeseen data corruptions. With the perturbation on the input side, the target label of a sample is smoothed with an exponentially decaying confidence level with respect to the size of the perturbation. ESP enforces a contour-like decision boundary that smoothly encompasses the region around inter-class samples. We theoretically show that perturbations in input space can encourage a model to find a flat minimum on the parameter space, which makes a model robust to domain shifts. In the extensive evaluation on common corruption benchmarks including MNIST-C, CIFAR-10/100-C, and Tiny-ImageNet-C, our method improves the robustness of a model both as a standalone method and in conjunction with the previous state-of-the-art augmentation-based methods. ESP is a model-agnostic algorithm in the sense that it is neither model-specific nor data-specific.