EdgeVisionBench

A Benchmark of Evolving Input Domains for Vision Applications at Edge

Conference Paper (2023)
Author(s)

Qinglong Zhang (Beijing Institute of Technology)

Rui Han (Beijing Institute of Technology)

Chi Harold Liu (Beijing Institute of Technology)

Guoren Wang (Beijing Institute of Technology)

Lydia Y. Chen (TU Delft - Data-Intensive Systems)

Research Group
Data-Intensive Systems
Copyright
© 2023 Qinglong Zhang, Rui Han, Chi Harold Liu, Guoren Wang, Lydia Y. Chen
DOI related publication
https://doi.org/10.1109/ICDE55515.2023.00288
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Qinglong Zhang, Rui Han, Chi Harold Liu, Guoren Wang, Lydia Y. Chen
Research Group
Data-Intensive Systems
Pages (from-to)
3643-3646
ISBN (print)
979-8-3503-2228-6
ISBN (electronic)
979-8-3503-2227-9
Reuse Rights

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Abstract

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.

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