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

• URL: http://arxiv.org/abs/2303.12424v1
• Date: Wed, 22 Mar 2023 09:51:08 GMT
• Title: Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning
• Authors: Dayuan Jian, Mohammad Rostami
• Abstract summary: Deep learning in event-based vision faces the challenge of annotated data scarcity due to recency of event cameras. We develop an unsupervised domain adaptation algorithm for training a deep network for event-based data image classification.
• Score: 12.013345715187285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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