  In this work, we focus on using convolution neural networks (CNN) to perform
object recognition on the event data. In object recognition, it is important
for a neural network to be robust to the variations of the data during testing.
For traditional cameras, translations are well handled because CNNs are
naturally equivariant to translations. However, because event cameras record
the change of light intensity of an image, the geometric shape of event volumes
will not only depend on the objects but also on their relative motions with
respect to the camera. The deformation of the events caused by motions causes
the CNN to be less robust to unseen motions during inference. To address this
problem, we would like to explore the equivariance property of CNNs, a
well-studied area that demonstrates to produce predictable deformation of
features under certain transformations of the input image.
