The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Knowledge Distillation via Instance Relationship Graph
Abstract
The key challenge of knowledge distillation is to extract general, moderate and sufficient knowledge from a teacher network to guide a student network. In this paper, a novel Instance Relationship Graph (IRG) is proposed for knowledge distillation. It models three kinds of knowledge, including instance features, instance relationships and feature space transformation, while the latter two kinds of knowledge are neglected by previous methods. Firstly, the IRG is constructed to model the distilled knowledge of one network layer, by considering instance features and instance relationships as vertexes and edges respectively. Secondly, an IRG transformation is proposed to models the feature space transformation across layers. It is more moderate than directly mimicking the features at intermediate layers. Finally, hint loss functions are designed to force a student's IRGs to mimic the structures of a teacher's IRGs. The proposed method effectively captures the knowledge along the whole network via IRGs, and thus shows stable convergence and strong robustness to different network architectures. In addition, the proposed method shows superior performance over existing methods on datasets of various scales.
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Cites
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Cited by
Knowledge Distillation: A Survey
Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931
Cross-Layer Distillation with Semantic Calibration
Chen, D., Mei, J.-P., Zhang, Y., Wang, C., Wang, Z., Feng, Y., & Chen, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16865 · Cited by 335
Multimodal learning with graphs
Ektefaie, Y., Dasoulas, G., Noori, A., Farhat, M., & Zitnik, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00624-6 · Cited by 166
A survey of model compression strategies for object detection
Lyu, Z., Yu, T., Pan, F., Zhang, Y., Luo, J., Zhang, D., Chen, Y., Zhang, B., & Li, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17192-x · Cited by 65
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Geng, X., Wang, Z., Chen, C., Xu, Q., Xu, K., Jin, C., Gupta, M., Yang, X., Chen, Z., Sabry Aly, M. M., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3394494 · Cited by 26
Metadata
- Authors
- Liu, Y., Cao, J., Li, B., Yuan, C., Hu, W., Li, Y., & Duan, Y.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 7089-7097
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2955192706
- Cited by
- 303
- References
- 27
- FWCI
- 12.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, Y., Cao, J., Li, B., Yuan, C., Hu, W., Li, Y., & Duan, Y. (2019). Knowledge Distillation via Instance Relationship Graph. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00726
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