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

Aditi Naiknaware, Sanchit Singh, Hajar Homayouni, Salimeh Sekeh

TL;DR

Temp-SCONE extends SCONE to open-world learning under temporal drift by introducing a confidence-driven temporal regularization that stabilizes predictions across evolving domains. It combines SCONE's energy-margin separation with ATC/AC-based loss to maintain strong ID performance while improving covariate shift robustness and semantic OOD detection in dynamic environments. Empirical results show significant gains on dynamic datasets (e.g., CLEAR, YearBook) with Vision backbones; on non-temporal, distinct datasets, temporal regularization offers limited benefit, highlighting the role of temporal continuity. Theoretical analysis derives a generalization bound linking temporal stability, OOD detection losses, and Fisher information, supporting Temp-SCONE as a step toward reliable open-world learning in evolving settings.

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.

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

TL;DR

Temp-SCONE extends SCONE to open-world learning under temporal drift by introducing a confidence-driven temporal regularization that stabilizes predictions across evolving domains. It combines SCONE's energy-margin separation with ATC/AC-based loss to maintain strong ID performance while improving covariate shift robustness and semantic OOD detection in dynamic environments. Empirical results show significant gains on dynamic datasets (e.g., CLEAR, YearBook) with Vision backbones; on non-temporal, distinct datasets, temporal regularization offers limited benefit, highlighting the role of temporal continuity. Theoretical analysis derives a generalization bound linking temporal stability, OOD detection losses, and Fisher information, supporting Temp-SCONE as a step toward reliable open-world learning in evolving settings.

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.

Paper Structure

This paper contains 11 sections, 8 theorems, 33 equations, 12 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Methodology
  3. Temp-SCONE Method
  4. Experiments
  5. Results and Discussion
  6. Theoretical Insights
  7. Related Work
  8. Conclusion
  9. Appendix
  10. Theoretical Proofs
  11. Additional Experiments

Key Result

Theorem 5.1

Let $\mathbb{P}^{t,cov}$ and $\mathbb{P}_{test}^{t,sem}$ be the covariate-shifted OOD and semantic OOD distribution. Denote $GErr_{t+1}(f)$ the generalization error at time $t$. Let $\mathcal{L}_{reg}$ be the OOD detection loss devised for MSP detectors hendrycks2018deep, i.e., cross-entropy between And $C_{t\rightarrow t+1}=C_{t+1}-C_t+B_t+Z_t$ and $\delta_t$ are constants and $\overline{\delta}_

Figures (12)

  • Figure 1: Dynamic Data (YearBook - 7 timesteps), FairFace is OOD data, (top row WRN, bottom row ViT). Columns show ID Acc.$\uparrow$, OOD Acc.$\uparrow$, FPR95 $\downarrow$.
  • Figure 2: Dynamic Data (CLEAR - 10 timesteps), Places365 is OOD data, (top row WRN, bottom row ViT). Columns show ID Acc.$\uparrow$, OOD Acc.$\uparrow$, FPR95 $\downarrow$.
  • Figure 3: Distinct Data - CIFAR-10 $\rightarrow$ Imagenette $\rightarrow$ CINIC-10 $\rightarrow$ STL-10 are four ID timesteps. Semantic OOD dataset changes with the timestep: timestep 1 uses LSUN-C, timestep 2 uses SVHN, timestep 3 uses Places365, and timestep 4 uses DTD (Textures), (top row WRN, bottom row ViT). Columns show ID Acc.$\uparrow$, OOD Acc.$\uparrow$, FPR95 $\downarrow$.
  • Figure 4: Dynamic Data (CLEAR - 10 timesteps), LSUN-C is OOD data, and Corruption type is Gaussian Noise (top row WRN, bottom row ViT). Columns show ID Acc.$\uparrow$, OOD Acc.$\uparrow$, FPR95 $\downarrow$.
  • Figure 5: Dynamic Data (CLEAR - 10 timesteps), LSUN-C is OOD data, and Corruption type is Defocus Blur (top row WRN, bottom row ViT). Columns show ID Acc.$\uparrow$, OOD Acc.$\uparrow$, FPR95 $\downarrow$.
  • ...and 7 more figures

Theorems & Definitions (8)

  • Theorem 5.1
  • Lemma 8.1
  • Lemma 8.2
  • Lemma 8.3
  • Lemma 8.4
  • Lemma 8.5
  • Lemma 8.6
  • Theorem 8.1