Learning Decision Trees Recurrently Through Communication
This addresses the need for interpretable AI in image classification, offering a novel approach that combines transparency with high performance, though it is incremental in integrating decision trees with neural networks.
The paper tackled the problem of making deep learning models interpretable without sacrificing accuracy by proposing a method that learns iterative binary sub-decisions to build a decision tree encoded in a Recurrent Neural Network, achieving state-of-the-art accuracy on benchmark datasets like ImageNet.
Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our model is its ability to build a decision tree whose structure is encoded into the memory representation of a Recurrent Neural Network jointly learned by two models communicating through message passing. In addition, our model assigns a semantic meaning to each decision in the form of binary attributes, providing concise, semantic and relevant rationalizations to the user. On three benchmark image classification datasets, including the large-scale ImageNet, our model generates human interpretable binary decision sequences explaining the predictions of the network while maintaining state-of-the-art accuracy.