# This Looks Like That: Deep Learning for Interpretable Image Recognition

- **Authors**: Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Jonathan Su, Cynthia Rudin
- **Year**: 2019
- **Summary**: This paper proposes a deep learning architecture that is inherently interpretable. The model explains its reasoning by pointing to prototypical parts of training examples that look similar to the input image. The explanation is in the form of "this looks like that," providing a case-based reasoning approach that is natural for humans to understand.
- **Link**: https://arxiv.org/abs/1806.10574
