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We present the Rate-Distortion Explanation (RDE) framework, a mathematicallywell-founded method for explaining black-box model decisions. The framework isbased on perturbations of the target input signal and applies to anydifferentiable pre-trained model such as neural networks. Our experimentsdemonstrate the framework's adaptability to diverse data modalities,particularly images, audio, and physical simulations of urban environments.

A Rate-Distortion Framework for Explaining Black-box Model Decisions