Corpus ID: 227254464

Neural Prototype Trees for Interpretable Fine-grained Image Recognition

@article{Nauta2020NeuralPT,
  title={Neural Prototype Trees for Interpretable Fine-grained Image Recognition},
  author={M. Nauta and Ron van Bree and C. Seifert},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.02046}
}
Interpretable machine learning addresses the black-box nature of deep neural networks. Visual prototypes have been suggested for intrinsically interpretable image recognition, instead of generating post-hoc explanations that approximate a trained model. However, a large number of prototypes can be overwhelming. To reduce explanation size and improve interpretability, we propose the Neural Prototype Tree (ProtoTree), a deep learning method that includes prototypes in an interpretable decision… Expand
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