EventAugment: Learning Augmentation Policies From Asynchronous Event-Based Data

Abstract

Data augmentation is an effective way to overcome the overfitting problem of deep learning models. However, most existing studies on data augmentation work on framelike data (e.g., images), and few tackles with event-based data. Event-based data are different from framelike data, rendering the augmentation techniques designed for framelike data unsuitable for event-based data. This work deals with data augmentation for event-based object classification and semantic segmentation, which is important for self-driving and robot manipulation. Specifically, we introduce EventAugment, a new method to augment asynchronous event-based data by automatically learning augmentation policies. We first identify 13 types of operations for augmenting event-based data. Next, we formulate the problem of finding optimal augmentation policies as a hyperparameter optimization problem. To tackle this problem, we propose a random search-based framework. Finally, we evaluate the proposed method on six public datasets including N-Caltech101, N-Cars, ST-MNIST, N-MNIST, DVSGesture, and DDD17. Experimental results demonstrate that EventAugment exhibits substantial performance improvements for both deep neural network-based and spiking neural network-based models, with gains of up to approximately 4%. Notably, EventAugment outperform state-of-the-art methods in terms of overall performance.