GLANCE: Global to Local Architecture-Neutral Concept-based Explanations

• URL: http://arxiv.org/abs/2207.01917v1
• Date: Tue, 5 Jul 2022 09:52:09 GMT
• Title: GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
• Authors: Avinash Kori, Ben Glocker, Francesca Toni
• Abstract summary: We propose a novel twin-surrogate explainability framework to explain the decisions made by any CNN-based image classifier. We first disentangle latent features from the classifier, followed by aligning these features to observed/human-defined context' features. These aligned features form semantically meaningful concepts that are used for extracting a causal graph depicting the perceived' data-generating process. We provide a generator to visualize the effect' of interactions among features in latent space and draw feature importance therefrom as local explanations.
• Score: 26.76139301708958
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most of the current explainability techniques focus on capturing the importance of features in input space. However, given the complexity of models and data-generating processes, the resulting explanations are far from being `complete', in that they lack an indication of feature interactions and visualization of their `effect'. In this work, we propose a novel twin-surrogate explainability framework to explain the decisions made by any CNN-based image classifier (irrespective of the architecture). For this, we first disentangle latent features from the classifier, followed by aligning these features to observed/human-defined `context' features. These aligned features form semantically meaningful concepts that are used for extracting a causal graph depicting the `perceived' data-generating process, describing the inter- and intra-feature interactions between unobserved latent features and observed `context' features. This causal graph serves as a global model from which local explanations of different forms can be extracted. Specifically, we provide a generator to visualize the `effect' of interactions among features in latent space and draw feature importance therefrom as local explanations. Our framework utilizes adversarial knowledge distillation to faithfully learn a representation from the classifiers' latent space and use it for extracting visual explanations. We use the styleGAN-v2 architecture with an additional regularization term to enforce disentanglement and alignment. We demonstrate and evaluate explanations obtained with our framework on Morpho-MNIST and on the FFHQ human faces dataset. Our framework is available at \url{https://github.com/koriavinash1/GLANCE-Explanations}.

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