A Survey on Learning to Reject

Abstract

Learning to reject is a special kind of self-awareness (the ability to know what you do not know), which is an essential factor for humans to become smarter. Although machine intelligence has become very accurate nowadays, it lacks such kind of self-awareness and usually acts as omniscient, resulting in overconfident errors. This article presents a comprehensive overview of this topic from three perspectives: confidence, calibration, and discrimination. Confidence is an important measurement for the reliability of model predictions. Rejection can be realized by setting thresholds on confidence. However, most models, especially modern deep neural networks, are usually overconfident. Therefore, calibration is a process to ensure confidence matching the actual likelihood of correctness, including two approaches: post-calibration and self-calibration. Calibration reflects the global characteristic of confidence, and the local distinguishing property of confidence is also important. In light of this, discrimination focuses on the performance of accepting positive samples while rejecting negative samples. As a binary classification problem, the challenge of discrimination comes from the missing and nonrepresentativeness of the negative data. Three discrimination tasks are comprehensively analyzed and discussed: failure rejection, unknown rejection, and fake rejection. By rejecting failures, the risk could be controlled especially for mission-critical applications. By rejecting unknowns, the awareness of the knowledge blind zone would be enhanced. By rejecting fakes, security and privacy could be protected. We provide a general taxonomy, organization, and discussion of the methods for solving these problems, which are studied separately in the literature. The connections between different approaches and future directions that are worth further investigation are also presented. With a discriminative and calibrated confidence, learning to reject will let the decision-making process be more practical, reliable, and secure.