Event Encoder

Event encoding focuses on efficiently representing streams of asynchronous events, often originating from sensors like event cameras or representing discrete occurrences in other domains (e.g., financial transactions, music). Current research emphasizes developing deep learning-based architectures, including autoencoders and deep belief networks, to achieve high compression ratios while preserving crucial information for downstream tasks like classification or 3D tracking. These advancements are improving data management and analysis in various fields, from computer vision and neuromorphic computing to crisis monitoring and network topology inference, by enabling efficient storage, transmission, and processing of event data. Furthermore, research is exploring optimal point-spread-function engineering and contrastive learning methods to enhance the quality and efficiency of event encoding.