Uncertainty Sets for Image Classifiers using Conformal Prediction
This provides reliable uncertainty quantification for image classifiers, enabling safer deployment in high-stakes applications, though it is an incremental improvement over existing conformal prediction methods.
The paper tackles the problem of quantifying uncertainty in convolutional image classifiers by introducing an algorithm that outputs predictive sets with formal coverage guarantees, achieving sets 5 to 10 times smaller than baselines on Imagenet datasets.
Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller than a stand-alone Platt scaling baseline.