DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation

• URL: http://arxiv.org/abs/2408.00331v1
• Date: Thu, 1 Aug 2024 07:08:11 GMT
• Title: DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation
• Authors: Rakshith Subramanyam, Kowshik Thopalli, Vivek Narayanaswamy, Jayaraman J. Thiagarajan,
• Abstract summary: We propose DECIDER, a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image models. DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient.
• Score: 18.77296551727931
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reliably detecting when a deployed machine learning model is likely to fail on a given input is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing Classifiers to Identify Errors Reliably), a novel approach that leverages priors from large language models (LLMs) and vision-language models (VLMs) to detect failures in image classification models. DECIDER utilizes LLMs to specify task-relevant core attributes and constructs a ``debiased'' version of the classifier by aligning its visual features to these core attributes using a VLM, and detects potential failure by measuring disagreement between the original and debiased models. In addition to proactively identifying samples on which the model would fail, DECIDER also provides human-interpretable explanations for failure through a novel attribute-ablation strategy. Through extensive experiments across diverse benchmarks spanning subpopulation shifts (spurious correlations, class imbalance) and covariate shifts (synthetic corruptions, domain shifts), DECIDER consistently achieves state-of-the-art failure detection performance, significantly outperforming baselines in terms of the overall Matthews correlation coefficient as well as failure and success recall. Our codes can be accessed at~\url{https://github.com/kowshikthopalli/DECIDER/}

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