DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation
<|reference_start|>DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation: 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/}<|reference_end|>
@article{subramanyam2024decider:,
    title={DECIDER: Leveraging Foundation Model Priors for Improved Model Failure
  Detection and Explanation},
    author={Rakshith Subramanyam, Kowshik Thopalli, Vivek Narayanaswamy, Jayaraman
  J.Thiagarajan},
    journal={arXiv preprint arXiv:2408.00331},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00331},
    primaryClass={cs.CV}
}