Iterative filter pruning with combined feature maps and knowledge distillation-Reference-Cited by-同舟云学术

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Iterative filter pruning with combined feature maps and knowledge distillation

Published:2024-09-06 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Liu Yajun,Fan Kefeng,Zhou Wenju

Funder

National Key Science and Technology Program of China 2030

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13042-024-02371-5.pdf

Reference60 articles.

1. Jeong S, Kim B, Cha S, Seo K, Chang H, Lee J, Kim Y, Noh J (2024) Real-time CNN training and compression for neural-enhanced adaptive live streaming. IEEE Trans Pattern Anal Mach Intell 46(9):6023–6039

2. Tian G, Liu L, Ri J, Liu Y, Sun Y (2019) ObjectFusion: an object detection and segmentation framework with RGB-D SLAM and convolutional neural networks. Neurocomputing 345:3–14

3. Jiang X, Wang N, Xin J, Xia X, Yang X, Gao X (2021) Learning lightweight super-resolution networks with weight pruning. Neural Netw 144:21–32

4. Sarvani CH, Ghorai M, Dubey SR, Basha SS (2022) Hrel: Filter pruning based on high relevance between activation maps and class labels. Neural Netw 147:186–197

5. Wang X, Zheng Z, He Y, Yan F, Zeng Z, Yang Y (2023) Progressive local filter pruning for image retrieval acceleration. IEEE Trans Multimed 25:9597–9607

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