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Event-based cameras offer reliable measurements for preforming computervision tasks in high-dynamic range environments and during fast motionmaneuvers. However, adopting deep learning in event-based vision faces thechallenge of annotated data scarcity due to recency of event cameras.Transferring the knowledge that can be obtained from conventional cameraannotated data offers a practical solution to this challenge. We develop anunsupervised domain adaptation algorithm for training a deep network forevent-based data image classification using contrastive learning anduncorrelated conditioning of data. Our solution outperforms the existingalgorithms for this purpose.

Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning