核心概念
Prioritizing interpretability in failure mode extraction is crucial for understanding model failures.
要約
The content discusses the importance of providing human-understandable descriptions for failure modes in image classification models. It introduces PRIME, a novel approach that prioritizes interpretability by obtaining human-understandable concepts (tags) of images and analyzing the model's behavior based on these tags. The method aims to improve the quality of text descriptions associated with failure modes through experiments on different datasets.
INTRODUCTION
Identifying failure modes crucial for reliable AI.
Existing methods lack human-understandable descriptions.
Importance of interpreting model failures.
DATA EXTRACTION
"Overall accuracy for images of class 'fox' is 81.96%."
"Model’s accuracy drops from 86.23% to 41.88% when all 3 tags 'hang', 'black', and 'branch' appear."
DETECTING FAILURE MODES
Obtaining relevant tags for images.
Evaluating model performance based on tag combinations.
Exhaustive search method used to identify failure modes.
EVALUATION
Generalization on unseen data and generated data.
Quality metrics for descriptions.
CLUSTERING-BASED METHODS CHALLENGES
Representation space may not align with semantic space.
Distance-based clustering struggles to generate coherent output.
統計
"Overall accuracy for images of class 'fox' is 81.96%."
"Model’s accuracy drops from 86.23% to 41.88% when all 3 tags 'hang', 'black', and 'branch' appear."