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Knowledge distillation has been applied to various tasks successfully.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3213–3223 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 2961–2969 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Earlier work this paper cites.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2881–2890 (2017)
2017
Earlier work this paper cites.
2019
Cited alongside, same era.
Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4794–4802 (2019)
2019
Cited alongside, same era.
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE TPAMI (2021). https://doi.org/10.1109/TPAMI.2019.2938758
2019
Cited alongside, same era.
He, T., Shen, C., Tian, Z., Gong, D., Sun, C., Yan, Y.: Knowledge adaptation for efficient semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 578–587 (2019)
2019
Cited alongside, same era.
Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: Solo: Segmenting objects by locations. In: European Conference on Computer Vision. pp. 649–665. Springer (2020)
2020
Later among the works it cites.
Yang, J., Martinez, B., Bulat, A., Tzimiropoulos, G.: Knowledge distillation via softmax regression representation learning. In: International Conference on Learning Representations (2020)
2020
Later among the works it cites.
Zhang, L., Ma, K.: Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors. In: International Conference on Learning Representations (2020)
2020
Later among the works it cites.
Zhou, H., Song, L., Chen, J., Zhou, Y., Wang, G., Yuan, J., Zhang, Q.: Rethinking soft labels for knowledge distillation: A bias–variance tradeoff perspective. In: International Conference on Learning Representations (2020)
2020
Later among the works it cites.
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Heo, B., Kim, J., Yun, S., Park, H., Kwak, N., Choi, J.Y.: A comprehensive overhaul of feature distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1921–1930 (2019)
2019
Cited alongside, same era.
Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z., Wang, J.: Structured knowledge distillation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2604–2613 (2019)
2019
Cited alongside, same era.
Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3967–3976 (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Wang, T., Yuan, L., Zhang, X., Feng, J.: Distilling object detectors with fine-grained feature imitation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4933–4942 (2019)
2019
Cited alongside, same era.
Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: Reppoints: Point set representation for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9657–9666 (2019)
2019
Cited alongside, same era.
Contributors, M.: Openmmlab’s image classification toolbox and benchmark. https://github.com/open-mmlab/mmclassification (2020)
2020
Cited alongside, same era.
Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5008–5017 (2021)
2021
Later among the works it cites.
Contributors, M.: Openmmlab model compression toolbox and benchmark. https://github.com/open-mmlab/mmrazor (2021)
2021
Later among the works it cites.
Dai, X., Jiang, Z., Wu, Z., Bao, Y., Wang, Z., Liu, S., Zhou, E.: General instance distillation for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7842–7851 (2021)
2021
Later among the works it cites.
Guo, J., Han, K., Wang, Y., Wu, H., Chen, X., Xu, C., Xu, C.: Distilling object detectors via decoupled features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2154–2164 (2021)
2021
Later among the works it cites.
Kang, Z., Zhang, P., Zhang, X., Sun, J., Zheng, N.: Instance-conditional knowledge distillation for object detection. In: In Proc. of the Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS) (2021)
2021
Later among the works it cites.
Shu, C., Liu, Y., Gao, J., Yan, Z., Shen, C.: Channel-wise knowledge distillation for dense prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5311–5320 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhixing, D., Zhang, R., Chang, M., Liu, S., Chen, T., Chen, Y., et al.: Distilling object detectors with feature richness. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.