Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
This work addresses interpretability needs in critical domains like healthcare, but it is incremental as it builds on existing adversarial example methods.
The paper tackles the problem of ambiguous neurons in deep neural networks (DNNs) by proposing an adversarial training algorithm with a consistent loss, resulting in improved interpretability on the whole image space.
Sometimes it is not enough for a DNN to produce an outcome. For example, in applications such as healthcare, users need to understand the rationale of the decisions. Therefore, it is imperative to develop algorithms to learn models with good interpretability (Doshi-Velez 2017). An important factor that leads to the lack of interpretability of DNNs is the ambiguity of neurons, where a neuron may fire for various unrelated concepts. This work aims to increase the interpretability of DNNs on the whole image space by reducing the ambiguity of neurons. In this paper, we make the following contributions: 1) We propose a metric to evaluate the consistency level of neurons in a network quantitatively. 2) We find that the learned features of neurons are ambiguous by leveraging adversarial examples. 3) We propose to improve the consistency of neurons on adversarial example subset by an adversarial training algorithm with a consistent loss.