C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets

Kangdao Liu, Hao Zeng, Jianguo Huang, Huiping Zhuang, Chi Man VONG, Hongxin Wei

Keywords: uncertainty estimation, conformal prediction, classification

Abstract:

Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers.
To optimize the classifier for maximum predictive efficiency, Conformal Training rectifies the training objective with a regularization that minimizes the average prediction set size at a specific error rate.
However, the regularization term inevitably deteriorates the classification accuracy and leads to suboptimal efficiency of conformal predictors.
To address this issue, we introduce \textbf{Conformal Adapter} (C-Adapter), an adapter-based tuning method to enhance the efficiency of conformal predictors without sacrificing accuracy.
In particular, we implement the adapter as a class of intra order-preserving functions and tune it with our proposed loss that maximizes the discriminability of non-conformity scores between correctly and randomly matched data-label pairs.
Using C-Adapter, the model tends to produce higher non-conformity scores for incorrect labels than for correct ones, thereby enhancing predictive efficiency across different coverage rates.
Extensive experiments show that C-Adapter can effectively adapt various classifiers for efficient prediction sets, as well as enhance the conformal training method.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 6200