Often machine learning models tend to automatically learn associations present in the training data without questioning their validity or appropriateness. This undesirable property is the root cause of the manifestation of spurious correlations, which render models unreliable and prone to failure in the presence of distribution shifts. Research shows that most methods attempting to remedy spurious correlations are only effective for a model's known spurious associations. Current spurious correlation detection algorithms either rely on extensive human annotations or are too restrictive in their formulation. Moreover, they rely on strict definitions of visual artifacts that may not apply to data produced by generative models, as they are known to hallucinate contents that do not conform to standard specifications. In this work, we introduce a general-purpose method that efficiently detects potential spurious correlations, and requires significantly less human interference in comparison to the prior art. Additionally, the proposed method provides intuitive explanations while eliminating the need for pixel-level annotations. We demonstrate the proposed method's tolerance to the peculiarity of AI-generated images, which is a considerably challenging task, one where most of the existing methods fall short. Consequently, our method is also suitable for detecting spurious correlations that may propagate to downstream applications originating from generative models.

翻译：机器学习模型往往倾向于自动学习训练数据中存在的关联，而不质疑其有效性或适当性。这种不良属性是虚假相关现象的根本原因，导致模型在分布偏移面前不可靠且容易失败。研究表明，大多数试图纠正虚假相关的方法仅对模型已知的虚假关联有效。现有的虚假相关检测算法要么依赖大量人工标注，要么在公式化表述上过于局限。此外，它们依赖于视觉伪影的严格定义，而这些定义可能不适用于生成模型产生的数据，因为生成模型已知会产生不符合标准规范的内容。在这项工作中，我们引入了一种通用方法，能够高效检测潜在的虚假相关，并相比现有技术显著减少人工干预。此外，所提出的方法提供直观的解释，同时消除了像素级标注的需求。我们证明了所提方法对AI生成图像特异性的容忍度，这是一个极具挑战性的任务——大多数现有方法在此任务中表现不足。因此，我们的方法也适用于检测可能从生成模型传播到下游应用的虚假相关。