The advantages of event-sensing over conventional sensors (e.g., higher
dynamic range, lower time latency, and lower power consumption) have spurred
research into machine learning for event data. Unsurprisingly, deep learning
has emerged as a competitive methodology for learning with event sensors; in
typical setups, discrete and asynchronous events are first converted into
frame-like tensors on which standard deep networks can be applied. However,
over-fitting remains a challenge, particularly since event datasets remain
small relative to conventional datasets (e.g., ImageNet). In this paper, we
introduce EventDrop, a new method for augmenting asynchronous event data to
improve the generalization of deep models. By dropping events selected with
various strategies, we are able to increase the diversity of training data
(e.g., to simulate various levels of occlusion). From a practical perspective,
EventDrop is simple to implement and computationally low-cost. Experiments on
two event datasets (N-Caltech101 and N-Cars) demonstrate that EventDrop can
significantly improve the generalization performance across a variety of deep
networks.Comment: IJCAI 202
