Representation Learning on Event Stream via an Elastic Net-incorporated
  Tensor Network
Beibei Yang Weiling Li Yan Fang
Event cameras are neuromorphic sensors that capture asynchronous and sparse
event stream when per-pixel brightness changes. The state-of-the-art processing
methods for event signals typically aggregate events into a frame or a grid.
However, events are dense in time, these works are limited to local information
of events due to the stacking. In this paper, we present a novel spatiotemporal
representation learning method which can capture the global correlations of all
events in the event stream simultaneously by tensor decomposition. In addition,
with the events are sparse in space, we propose an Elastic Net-incorporated
tensor network (ENTN) model to obtain more spatial and temporal details about
event stream. Empirically, the results indicate that our method can represent
the spatiotemporal correlation of events with high quality, and can achieve
effective results in applications like filtering noise compared with the
state-of-the-art methods.