A Framework to Learn with Interpretation
This addresses the problem of interpretability for deep learning practitioners, offering a method to generate interpretable models by design or provide post-hoc interpretations, though it appears incremental as it builds on existing interpretability techniques.
The authors tackled interpretability in deep learning by developing a framework that jointly learns a predictive model and an interpretation model, achieving minimal accuracy loss while providing local and global interpretability through high-level attribute functions.
To tackle interpretability in deep learning, we present a novel framework to jointly learn a predictive model and its associated interpretation model. The interpreter provides both local and global interpretability about the predictive model in terms of human-understandable high level attribute functions, with minimal loss of accuracy. This is achieved by a dedicated architecture and well chosen regularization penalties. We seek for a small-size dictionary of high level attribute functions that take as inputs the outputs of selected hidden layers and whose outputs feed a linear classifier. We impose strong conciseness on the activation of attributes with an entropy-based criterion while enforcing fidelity to both inputs and outputs of the predictive model. A detailed pipeline to visualize the learnt features is also developed. Moreover, besides generating interpretable models by design, our approach can be specialized to provide post-hoc interpretations for a pre-trained neural network. We validate our approach against several state-of-the-art methods on multiple datasets and show its efficacy on both kinds of tasks.