Joint Classification and Unknown Detection Using Class Conditional Probability Calibration

The closed set assumption, where training classes are fixed at inference, is often impractical as deployed models face open-set conditions with unknown classes. This challenge drives the field of Open-Set Recognition (OSR), which aims to identify unknown samples during inference. A common approach to OSR involves training on exemplars of unknown objects (also referred to as known unknowns), which are examples that do not belong to the closed set of known classes. However, this is infeasible for methods that rely solely on the training samples of the known classes. For such cases, we show that the OSR problem can be effectively tackled by combining the decision confidences of two networks: one trained with softmax cross-entropy and the other with tuplet loss using class anchors. We show that the proposed approach outperforms individual methods across OSR benchmarks, maintaining correct classification and high confidence for known samples while effectively rejecting unknowns.