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Knowledge-Distillation-Warm-Start Training Strategy for Lightweight Super-Resolution Networks

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1966))

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Abstract

In recent years, studies on lightweight networks have made rapid progress in the field of image Super-Resolution (SR). Although the lightweight SR network is computationally efficient and saves parameters, the simplification of the structure inevitably leads to limitations in its performance. To further enhance the efficacy of lightweight networks, we propose a Knowledge-Distillation-Warm-Start (KDWS) training strategy. This strategy enables further optimization of lightweight networks using dark knowledge from traditional large-scale SR networks during warm-start training and can empirically improve the performance of lightweight models. For experiment, we have chosen several traditional large-scale SR networks and lightweight networks as teacher and student networks, respectively. The student network is initially trained with a conventional warm-start strategy, followed by additional supervision from the teacher network for further warm-start training. The evaluation on common test datasets shows that our proposed training strategy can result in better performance for a lightweight SR network. Furthermore, our proposed approach can also be adopted in any deep learning network training process, not only image SR tasks, as it is not limited by network structure or task type.

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Acknowledgments

: This work is supported by the Ministry of Science and Technology of China (No. G2022036009L), Open Fund of Intelligent Terminal Key Laboratory of Sichuan Province (No. SCTLAB-2007), Yibin Science and Technology Program (No. 2021CG003) and Science and Technology Program of Yibin Sanjiang New Area (No. 2023SJXQYBKJJH001).

Author information

Authors and Affiliations

  1. Sichuan Artificial Intelligence Research Institute, Yibin, 644000, China

    Min Lei, Kun He, Hui Xu & Jie Shao

  2. University of Electronic Science and Technology of China, Chengdu, 611731, China

    Min Lei, Kun He, Hui Xu & Jie Shao

  3. Intelligent Terminal Key Laboratory of Sichuan Province, Yibin, 644000, China

    Hui Xu

  4. Yibin Great Technology Co., Ltd., Yibin, 644000, China

    Yunfeng Yang

Authors
  1. Min Lei
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  2. Kun He
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  3. Hui Xu
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  4. Yunfeng Yang
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  5. Jie Shao
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Corresponding author

Correspondence to Hui Xu .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Lei, M., He, K., Xu, H., Yang, Y., Shao, J. (2024). Knowledge-Distillation-Warm-Start Training Strategy for Lightweight Super-Resolution Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_22

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  • DOI: https://doi.org/10.1007/978-981-99-8148-9_22

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  • Online ISBN: 978-981-99-8148-9

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