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Co-active: an efficient selective relabeling model for resource constrained edge AI

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Abstract

With high-quality annotation data, edge AI has emerged as a pivotal technology in various domains. Unfortunately, due to sensor errors and discrepancies in data collection, datasets often suffer from noisy labels. Identifying and relabeling all the noisy data becomes imperative, but it’s labor-intensive and time-consuming. To ensure the robustness of resource-constrained edge AI models with noisy labels, in this paper, we propose an efficient selective relabeling method

leveraging expert knowledge, termed “Co-active”. The method involves three important steps: Noisy Data Identification, Informative Data Selection, and Model Re-training. Initially, we pre-train an encoder model with early stopping to detect noisy instances by analyzing the prediction discrepancies between two classifiers that have been initialized differently. Then we select the most informative noisy data from previous instances by introducing a novel priority scorer that combines entropy and dynamic loss variations. Following this, we utilize the mixup technique to retrain the model. The retraining process involves a dataset that is a composite of clean, relabeled, and data that is potentially clean. This is facilitated by a novel loss function that adeptly balances classification accuracy with regularization terms. Additionally, we introduce strategies for dynamically adjusting the size of the relabel dataset to optimize the labeling budget and enhance model robustness. Our extensive experiments across four datasets demonstrate that Co-active consistently achieves the best performance, with an average improvement of 18.67%.

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Data Availability

The data and materials for this study are readily available upon request, ensuring transparency and reproducibility for interested researchers.

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Acknowledgements

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Funding

This research is partially supported by China Postdoctoral Science Foundation (No. 2023M730347).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Chenyu Hou, Kai Jiang & Tiantian Li

  2. Zhejiang Development & Planning Institute, Hangzhou, China

    Kai Jiang

  3. Anhui Tobacco Company, Bengbu Branch, Bengbu, China

    Meng Zhou

  4. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Jun Jiang

  5. Eastcom Co., Ltd, Hangzhou, China

    Jun Jiang

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  1. Chenyu Hou
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Contributions

Chenyu Hou designed the study and wrote the main manuscript text; Kai Jiang conducted the experiments, and collected and analyzed the data; Tiantian Li was responsible for the preparation of some experimental materials and conducted specific experimental procedures; Meng Zhou and Jun Jiang collected the data and performed the statistical analysis for the study; All authors reviewed the manuscript.

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Correspondence to Kai Jiang.

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Hou, C., Jiang, K., Li, T. et al. Co-active: an efficient selective relabeling model for resource constrained edge AI. Wireless Netw 31, 2653–2666 (2025). https://doi.org/10.1007/s11276-025-03903-9

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