Generating Human-Interpretable Rules from Convolutional Neural Networks

Advancements in the field of artificial intelligence have been rapid in recent years and have revolutionized various industries. Various deep neural network architectures capable of handling both text and images, covering code generation from natural language as well as producing machine translation and text summaries, have been proposed. For example, convolutional neural networks or CNNs perform image classification at a level equivalent to that of humans on many image datasets. These state-of-the-art networks have reached unprecedented levels of success by using complex architectures with billions of parameters, numerous kernel configurations, weight initialization, and regularization methods. Unfortunately to reach this level of success, the models that CNNs use are essentially black box in nature, with little or no human-interpretable information on the decision-making process. This lack of transparency in decision making gave rise to concerns amongst some sectors of the user community such as healthcare, finance, justice, and defense, among others. This challenge motivated our research, where we successfully produced human-interpretable influential features from CNNs for image classification and captured the interactions between these features by producing a concise decision tree making that makes classification decisions. The proposed methodology makes use of a pretrained VGG-16 with fine-tuning to extract feature maps produced by learnt filters. On the CelebA image benchmark dataset, we successfully produced human-interpretable rules that captured the main facial landmarks responsible for segmenting men from women with 89.6% accuracy, while on the more challenging Cats vs. Dogs dataset, the decision tree achieved 87.6% accuracy.