Towards Visual Explanations for Convolutional Neural Networks via Input Resampling
This work addresses the interpretability challenge in neural networks for researchers and practitioners, but it is incremental as it builds on existing explanation techniques.
The paper tackles the problem of interpreting convolutional neural networks by proposing a framework that analyzes predictions through internal features using two neuron selection metrics based on input perturbations, aiming to reveal attention mechanisms.
The predictive power of neural networks often costs model interpretability. Several techniques have been developed for explaining model outputs in terms of input features; however, it is difficult to translate such interpretations into actionable insight. Here, we propose a framework to analyze predictions in terms of the model's internal features by inspecting information flow through the network. Given a trained network and a test image, we select neurons by two metrics, both measured over a set of images created by perturbations to the input image: (1) magnitude of the correlation between the neuron activation and the network output and (2) precision of the neuron activation. We show that the former metric selects neurons that exert large influence over the network output while the latter metric selects neurons that activate on generalizable features. By comparing the sets of neurons selected by these two metrics, our framework suggests a way to investigate the internal attention mechanisms of convolutional neural networks.