Keywords: Explainable AI, interpretable model, pixel attributing, convolutional neural networks
TL;DR: This paper proposes a GMM-based probablistic model to explain DCNN representations and inference by proxy models and explanatory examples.
Abstract: Post-hoc explanations of deep neural networks improve human understanding on the learned representations, decision-making process and uncertainty of the model with faithfulness. Explaining deep convolutional neural networks(DCNN) is especially challenging, due to the high dimensionality of deep features and the complexity of model inference. Most post-hoc explaining methods serve a single form of explanation, restricting the diversity and consistency of the explanation. This paper proposes joint Gaussian mixture model(JGMM), a probabilistic model jointly models inter-layer deep features and produces faithful and consistent post-hoc explanations. JGMM explains deep features by Gaussian mixture model and inter-layer deep feature relations by posterior distribution on the latent component variables. JGMM enables a versatile explaining framework that unifies interpretable proxy model, global or local explanatory example generation or mining. Experiments are performed on various DCNN image classifiers in comparison with other explaining methods. It shows that JGMM can efficiently produce versatile, consistent, faithful and understandable explanations.
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