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Zero-shot Model Diagnosis that generates counterfactual samples and sensitivity analysis.
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Zero-shot Model Diagnosis Z er o -sh o t M odel Diagnosis (ZOOM) CVPR 2023 Jinqi Luo * , Zhaoning Wang * , Chen Henry Wu , Dong Huang , Fernando De La Torre School of Computer Science Carnegie Mellon University Paper arXiv Code How can we diagnose a deep learning computer vision model without a test set ? Diagnose your vision model's failure by just typing the attributes of interest. Our Plug-and-play framework generates a histogram of sensitivity analysis. Abstract When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expens...
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