Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations
This addresses the need for interpretability in safety-critical applications of deep learning, though it is incremental as it builds on existing causal and autoencoder techniques.
The paper tackled the problem of explaining deep neural network predictions by constructing causal models using autoencoders to extract human-understandable concepts from network activations, resulting in a method to identify and visualize features with significant causal influence on image classification.
Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then build a bayesian causal model using these extracted concepts as variables in order to explain image classification. Finally, we use this causal model to identify and visualize features with significant causal influence on final classification.