Conformal Prediction for Long-Tailed Classification

TL;DR

This paper introduces new conformal prediction methods tailored for long-tailed classification problems, balancing class-conditional coverage and set size, demonstrated on large-scale image datasets.

Contribution

It proposes a prevalence-adjusted softmax score and an interpolation procedure to improve conformal prediction in long-tailed settings.

Findings

Achieved better class-conditional coverage with smaller sets

Demonstrated effectiveness on large-scale datasets

Provided flexible trade-offs between coverage and set size

Abstract

Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets to be useful in such settings, they should (i) provide good class-conditional coverage, ensuring that rare classes are not systematically omitted from the prediction sets, and (ii) be a reasonable size, allowing users to easily verify candidate labels. Unfortunately, existing conformal prediction methods, when applied to the long-tailed setting, force practitioners to make a binary choice between small sets with poor class-conditional coverage or sets that have very good class-conditional coverage but are extremely large. We propose methods with marginal coverage guarantees that smoothly trade off set size and class-conditional coverage. First, we introduce a new conformal score function called prevalence-adjusted softmax that optimizes for macro-coverage, defined as the average class-conditional coverage across classes. Second, we propose a new procedure that interpolates between marginal and class-conditional conformal prediction by linearly interpolating their conformal score thresholds. We demonstrate our methods on Pl@ntNet-300K and iNaturalist-2018, two long-tailed image datasets with 1,081 and 8,142 classes, respectively.