Test-Time Error Minimization for Open Set Recognition

The success of supervised learning largely stems from explicit error minimization. In contrast, Test-Time Adaptation lacks ground-truth labels, precluding direct optimization. This work enables explicit error minimization for Test-Time Open-Set Recognition (TOSR). Specifically, we identify a previously overlooked fact: under a fixed recall, the challenging objective of Test-Time Error Minimization is equivalent to the tractable objective of Negative Prediction Maximization. Based on that, our algorithm learns a set of discrete boundaries and searches for their ensemble to maximize negative predictions. Experiments demonstrate that our method successfully closes around 50% of the performance gap between TOSR methods and supervised learning on several benchmarks.