Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors

TL;DR

This paper introduces SuperMentor, an advanced error prediction framework for AI models that improves understanding and anticipation of mistakes across in-domain, out-of-domain, and adversarial scenarios, enhancing AI reliability.

Contribution

The paper presents a novel mentor-mentee framework with transformer-based models and an oracle SuperMentor that significantly improves error prediction across diverse datasets and model architectures.

Findings

Mentor models excel at predicting adversarial errors with small perturbations.

Transformer-based mentors outperform other architectures in error prediction.

SuperMentor surpasses baseline mentors in predicting errors across various scenarios.

Abstract

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.