Interpretable Image Recognition by Screening Class-Specific and Class-Shared Prototypes

Author:

Li XiaomengORCID,Wang JiaqiORCID,Jing LipingORCID

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Bai, J., et al.: Rectified decision trees: towards interpretability, compression and empirical soundness. arXiv preprint arXiv:1903.05965 (2019)

2. Chen, B., et al.: An investigation of how label smoothing affects generalization. arXiv preprint arXiv:2010.12648 (2020)

3. Chen, C., et al.: This looks like that: deep learning for interpretable image recognition. Adv. Neural Inf. Process. Syst. 32, 1–12 (2019)

4. Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)

5. Donnelly, J., Barnett, A.J., Chen, C.: Deformable protopnet: an interpretable image classifier using deformable prototypes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10265–10275 (2022)

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