Implicit Mixture of Interpretable Experts for Global and Local Interpretability
This addresses interpretability in image classification for researchers and practitioners, but it is incremental as it builds on existing MoIE frameworks.
The paper tackled the problem of mixtures of interpretable experts (MoIE) learning to 'cheat' by having a black-box router solve classification alone, and solved it by introducing interpretable routers and an implicit parameterization scheme, resulting in a model (IMoIE) that matches state-of-the-art accuracy on MNIST10 with local interpretability.
We investigate the feasibility of using mixtures of interpretable experts (MoIE) to build interpretable image classifiers on MNIST10. MoIE uses a black-box router to assign each input to one of many inherently interpretable experts, thereby providing insight into why a particular classification decision was made. We find that a naively trained MoIE will learn to 'cheat', whereby the black-box router will solve the classification problem by itself, with each expert simply learning a constant function for one particular class. We propose to solve this problem by introducing interpretable routers and training the black-box router's decisions to match the interpretable router. In addition, we propose a novel implicit parameterization scheme that allows us to build mixtures of arbitrary numbers of experts, allowing us to study how classification performance, local and global interpretability vary as the number of experts is increased. Our new model, dubbed Implicit Mixture of Interpretable Experts (IMoIE) can match state-of-the-art classification accuracy on MNIST10 while providing local interpretability, and can provide global interpretability albeit at the cost of reduced classification accuracy.