Knowledge Distillation of Convolutional Neural Networks through Feature Map Transformation using Decision Trees
This work addresses the lack of transparency in DNNs for real-world applications like medical imaging, though it is incremental as it applies existing distillation methods to new data.
The authors tackled the interpretability problem of deep neural networks by distilling knowledge from CNNs into decision trees using feature map transformation, achieving comparable performance on medical image datasets with reduced complexity.
The interpretation of reasoning by Deep Neural Networks (DNN) is still challenging due to their perceived black-box nature. Therefore, deploying DNNs in several real-world tasks is restricted by the lack of transparency of these models. We propose a distillation approach by extracting features from the final layer of the convolutional neural network (CNN) to address insights to its reasoning. The feature maps in the final layer of a CNN are transformed into a one-dimensional feature vector using a fully connected layer. Subsequently, the extracted features are used to train a decision tree to achieve the best accuracy under constraints of depth and nodes. We use the medical images of dermaMNIST, octMNIST, and pneumoniaMNIST from the medical MNIST datasets to demonstrate our proposed work. We observed that performance of the decision tree is as good as a CNN with minimum complexity. The results encourage interpreting decisions made by the CNNs using decision trees.