Understanding CNN Hidden Neuron Activations Using Structured Background Knowledge and Deductive Reasoning
This work addresses the problem of explainability in deep learning for researchers and practitioners, offering a systematic approach to demystify black-box models, though it is incremental in building on existing symbolic reasoning techniques.
The paper tackles the challenge of interpreting hidden neuron activations in CNNs by introducing an automated method that uses structured background knowledge and deductive reasoning to assign meaningful labels to neurons, demonstrating its effectiveness in providing interpretable insights.
A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would provide insights into the question of what a deep learning system has internally detected as relevant on the input, demystifying the otherwise black-box character of deep learning systems. The state of the art indicates that hidden node activations can, in some cases, be interpretable in a way that makes sense to humans, but systematic automated methods that would be able to hypothesize and verify interpretations of hidden neuron activations are underexplored. In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. Our approach is based on using large-scale background knowledge approximately 2 million classes curated from the Wikipedia concept hierarchy together with a symbolic reasoning approach called Concept Induction based on description logics, originally developed for applications in the Semantic Web field. Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.