Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods overlook evolving domains and under represent their shifts in label and feature distributions. To address this gap, we present EdgeVisionBench, a benchmark seeking to generate evolving domains of various types and reflect their realistic label and feature shifts encountered by edge-based vision applications. To facilitate evaluating domain adaptation methods on edge devices, we provide an open-source package that automates workload generation, contains popular DNN models and compression techniques, and standardizes evaluations with interactive interfaces. Code and datasets are available at https://github.com/LINC-BIT/EdgeVisionBench.
|Title of host publication||Proceedings of the 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023|
|Place of Publication||Piscataway|
|Number of pages||4|
|Publication status||Published - 2023|
|Event||39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States|
Duration: 3 Apr 2023 → 7 Apr 2023
|Name||Proceedings - International Conference on Data Engineering|
|Conference||39th IEEE International Conference on Data Engineering, ICDE 2023|
|Period||3/04/23 → 7/04/23|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Edge computing
- evolving domains
- vision applications