快速图像分层博弈
Fast Hierarchical Games for Image Explanations
摘要：
随着现代复杂的神经网络不断打破记录并解决更难的问题，它们的预测也变得越来越难以理解。目前缺乏可解释性通常会破坏在敏感环境中部署准确的机器学习工具。在这项工作中，我们提出了一个模型不可知的解释方法的基础上层次扩展Shapley系数-层次形状（h-Shap）的图像分类-解决了一些目前的方法的局限性。与其他基于Shapley的解释方法不同，h-Shap是可扩展的，并且可以在不需要近似的情况下计算。在某些分布假设下，例如多实例学习中常见的假设，h-Shap检索精确的Shapley系数，计算复杂性呈指数级改善。我们比较了我们的分层方法与流行的基于Shapley和非基于Shapley的方法在合成数据集，医学成像场景，和一般的计算机视觉问题，表明h-Shap在准确性和运行时间方面都优于最先进的方法。代码和实验是公开的。
英文摘要：
As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible. The current lack of interpretability often undermines the deployment of accurate machine learning tools in sensitive settings. In this work, we present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients--Hierarchical Shap (h-Shap)--that resolves some of the limitations of current approaches. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. Under certain distributional assumptions, such as those common in multiple instance learning, h-Shap retrieves the exact Shapley coefficients with an exponential improvement in computational complexity. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem, showing that h-Shap outperforms the state of the art in both accuracy and runtime. Code and experiments are made publicly available.
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