Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift

• URL: http://arxiv.org/abs/2512.04571v1
• Date: Thu, 04 Dec 2025 08:35:51 GMT
• Title: Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift
• Authors: Aditi Naiknaware, Sanchit Singh, Hajar Homayouni, Salimeh Sekeh,
• Abstract summary: We propose Temp-SCONE, a temporally consistent extension of SCONE to handle temporal shifts in dynamic environments.<n>Experiments on dynamic datasets demonstrate that Temp-SCONE significantly improves under temporal drift.<n>Our theoretical insights on temporal stability and generalization error further establish Temp-SCONE as a step toward reliable OWL.
• Score: 0.0874967598360817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-world learning (OWL) requires models that can adapt to evolving environments while reliably detecting out-of-distribution (OOD) inputs. Existing approaches, such as SCONE, achieve robustness to covariate and semantic shifts but assume static environments, leading to degraded performance in dynamic domains. In this paper, we propose Temp-SCONE, a temporally consistent extension of SCONE designed to handle temporal shifts in dynamic environments. Temp-SCONE introduces a confidence-driven regularization loss based on Average Thresholded Confidence (ATC), penalizing instability in predictions across time steps while preserving SCONE's energy-margin separation. Experiments on dynamic datasets demonstrate that Temp-SCONE significantly improves robustness under temporal drift, yielding higher corrupted-data accuracy and more reliable OOD detection compared to SCONE. On distinct datasets without temporal continuity, Temp-SCONE maintains comparable performance, highlighting the importance and limitations of temporal regularization. Our theoretical insights on temporal stability and generalization error further establish Temp-SCONE as a step toward reliable OWL in evolving dynamic environments.

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