  Event-based cameras offer reliable measurements for preforming computer
vision tasks in high-dynamic range environments and during fast motion
maneuvers. However, adopting deep learning in event-based vision faces the
challenge of annotated data scarcity due to recency of event cameras.
Transferring the knowledge that can be obtained from conventional camera
annotated data offers a practical solution to this challenge. We develop an
unsupervised domain adaptation algorithm for training a deep network for
event-based data image classification using contrastive learning and
uncorrelated conditioning of data. Our solution outperforms the existing
algorithms for this purpose.
