用一个模型预测AI在各种情况下的错误，提升系统可靠性。
Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors
- 用'导师模型'学习主模型的错误模式。
- 在小扰动对抗样本上预测准确率超90%。
- 适合需要高可靠性的医疗、自动驾驶场景。
AI模型在识别图像时会出错，无论图像是否在训练数据分布内、分布外或受到微小扰动的对抗攻击。准确预测这些错误对提高系统可靠性、减少高成本错误并实现实时修正至关重要。然而，理解模型为何出错、如何预测仍具挑战性。本文通过一个‘导师’模型——即深度神经网络，用于预测另一个‘学徒’模型的错误——进行了全面的实证评估。结果表明，该导师模型在学习学徒模型在小扰动对抗图像上的错误方面表现优异，并能有效泛化至预测学徒模型在分布内和分布外的错误。此外，基于Transformer的导师模型在不同学徒架构上均表现出色。基于这些发现，我们构建了一个名为SuperMentor的‘预言家’式导师模型，能在ImageNet-1K数据集上超越基线模型，对多种错误类型实现更优预测。本框架为未来研究人工智能行为预判与纠正奠定了基础，有助于增强对AI系统的信任。
原文摘要 · Abstract (English)
AI models make mistakes when recognizing images-whether in-domain, out-of-domain, or adversarial. Predicting these errors is critical for improving system reliability, reducing costly mistakes, and enabling proactive corrections in real-world applications such as healthcare, finance, and autonomous systems. However, understanding what mistakes AI models make, why they occur, and how to predict them remains an open challenge. Here, we conduct comprehensive empirical evaluations using a "mentor" model-a deep neural network designed to predict another "mentee" model's errors. Our findings show that the mentor excels at learning from a mentee's mistakes on adversarial images with small perturbations and generalizes effectively to predict in-domain and out-of-domain errors of the mentee. Additionally, transformer-based mentor models excel at predicting errors across various mentee architectures. Subsequently, we draw insights from these observations and develop an "oracle" mentor model, dubbed SuperMentor, that can outperform baseline mentors in predicting errors across different error types from the ImageNet-1K dataset. Our framework paves the way for future research on anticipating and correcting AI model behaviors, ultimately increasing trust in AI systems.
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