因果驱动的模型鲁棒性审计
Causality-Driven Audits of Model Robustness
AI总结：
本文提出一种基于因果推断的DNN鲁棒性审计新方法，通过因果模型编码成像因素及其交互，仅用观测数据估计各因素对性能的因果效应，以应对真实世界复杂失真，降低部署风险。
摘要：
深度神经网络（DNN）的鲁棒性审计提供了一种揭示模型对具有挑战性的真实世界成像条件敏感性的手段，这些条件会显著降低DNN在野外环境中的性能。此类条件通常是环境、传感器或处理流程中固有的多个相互作用因素的结果，并可能导致难以分类的复杂图像失真。当鲁棒性审计仅限于一组孤立的成像效应或失真时，其结果（难以）轻松转移到图像损坏可能更复杂或更细微的真实世界条件中。为应对这一挑战，我们提出了一种新的替代性鲁棒性审计方法，该方法使用因果推断来测量DNN对导致复杂失真的成像过程因素的敏感性。我们的方法使用因果模型显式编码关于领域相关因素及其相互作用的假设。然后，通过在多个视觉任务上对自然图像和渲染图像进行广泛实验，我们表明我们的方法仅使用观测性领域数据即可可靠地估计每个因素对DNN性能的因果效应。这些因果效应直接将DNN的敏感性与感兴趣领域中成像流程的可观测属性联系起来，从而降低在该领域部署时DNN意外失败的风险。
英文摘要：
Robustness audits of deep neural networks (DNN) provide a means to uncover model sensitivities to the challenging real-world imaging conditions that significantly degrade DNN performance in-the-wild. Such conditions are often the result of multiple interacting factors inherent to the environment, sensor, or processing pipeline and may lead to complex image distortions that are not easily categorized. When robustness audits are limited to a set of isolated imaging effects or distortions, the results cannot be (easily) transferred to real-world conditions where image corruptions may be more complex or nuanced. To address this challenge, we present a new alternative robustness auditing method that uses causal inference to measure DNN sensitivities to the factors of the imaging process that cause complex distortions. Our approach uses causal models to explicitly encode assumptions about the domain-relevant factors and their interactions. Then, through extensive experiments on natural and rendered images across multiple vision tasks, we show that our approach reliably estimates causal effects of each factor on DNN performance using only observational domain data. These causal effects directly tie DNN sensitivities to observable properties of the imaging pipeline in the domain of interest towards reducing the risk of unexpected DNN failures when deployed in that domain.
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