6 research outputs found
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
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 tree to faithfully visualize the entire model. In
addition to global interpretability, a path in the tree explains a single
prediction. Each node in our binary tree contains a trainable prototypical
part. The presence or absence of this prototype in an image determines the
routing through a node. Decision making is therefore similar to human
reasoning: Does the bird have a red throat? And an elongated beak? Then it's a
hummingbird! We tune the accuracy-interpretability trade-off using ensembling
and pruning. We apply pruning without sacrificing accuracy, resulting in a
small tree with only 8 prototypes along a path to classify a bird from 200
species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the
CUB-200-2011 and Stanford Cars data sets. Code is available at
https://github.com/M-Nauta/ProtoTreeComment: 11 pages, and 9 pages supplementar
A Survey of Neural Trees
Neural networks (NNs) and decision trees (DTs) are both popular models of
machine learning, yet coming with mutually exclusive advantages and
limitations. To bring the best of the two worlds, a variety of approaches are
proposed to integrate NNs and DTs explicitly or implicitly. In this survey,
these approaches are organized in a school which we term as neural trees (NTs).
This survey aims to present a comprehensive review of NTs and attempts to
identify how they enhance the model interpretability. We first propose a
thorough taxonomy of NTs that expresses the gradual integration and
co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their
interpretability and performance, and suggest possible solutions to the
remaining challenges. Finally, this survey concludes with a discussion about
other considerations like conditional computation and promising directions
towards this field. A list of papers reviewed in this survey, along with their
corresponding codes, is available at:
https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl