Towards Explanation of DNN-based Prediction with Guided Feature Inversion
This addresses the problem of interpretability in DNNs for applications like health informatics, though it is incremental by building on existing local interpretation methods.
The paper tackles the lack of interpretability in deep neural network predictions by proposing a guided feature inversion framework that determines feature contributions and provides class-discriminative insights into decision-making, demonstrating effectiveness on ImageNet and PASCAL VOC07 datasets.
While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics. Existing attempts based on local interpretations aim to identify relevant features contributing the most to the prediction of DNN by monitoring the neighborhood of a given input. They usually simply ignore the intermediate layers of the DNN that might contain rich information for interpretation. To bridge the gap, in this paper, we propose to investigate a guided feature inversion framework for taking advantage of the deep architectures towards effective interpretation. The proposed framework not only determines the contribution of each feature in the input but also provides insights into the decision-making process of DNN models. By further interacting with the neuron of the target category at the output layer of the DNN, we enforce the interpretation result to be class-discriminative. We apply the proposed interpretation model to different CNN architectures to provide explanations for image data and conduct extensive experiments on ImageNet and PASCAL VOC07 datasets. The interpretation results demonstrate the effectiveness of our proposed framework in providing class-discriminative interpretation for DNN-based prediction.