Joint Classification and Unknown Detection using Class Conditional Probability Calibration
Published in International Symposium on Signals, Circuits and Systems, 2025
Recommended citation: D Brignac, A Cuellar, B Latibari, A Mahalanobis, "Joint Classification and Unknown Detection using Class Conditional Probability Calibration,"2025 International Symposium on Signals, Circuits and Systems.
Open-Set Recognition (OSR) addresses the challenge of deployed models encountering unknown classes beyond their fixed training set, often by leveraging known unknowns, though this is not always feasible. We propose combining the decision confidences of a softmax cross-entropy network and a tuplet-loss class-anchor network, achieving superior performance across OSR benchmarks by accurately classifying known samples while reliably rejecting unknowns.