Title: Conformal Uncertainty Indicator for Continual Test-Time Adaptation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Test-time adaptation, Uncertainty measurement, continual learning

Abstract: Continual Test-Time Adaptation (CTTA) enables models to adapt to sequential domain shifts during testing, but reliance on pseudo-labels makes them prone to error accumulation. Reliable uncertainty estimation is thus critical. We study this problem under the calibration-aided CTTA setting, where a small calibration buffer from the source domain is available as reference. We propose the Conformal Uncertainty Indicator (CUI), a plug-and-play method that leverages Conformal Prediction (CP) with calibration data. Unlike standard CP, which suffers from a coverage gap under domain shifts, CUI jointly measures model shift and data shift to adjust conformal quantiles and restore coverage. The resulting prediction set size provides a reliable indicator of test-time uncertainty. Building on this, we introduce a CUI-guided adaptation strategy that updates models only on confident samples. Experiments on three benchmarks show that CUI achieves accurate uncertainty estimation and improves the robustness of multiple CTTA baselines.

TL;DR: This paper proposes a Conformal Uncertainty Indicator (CUI) for CTTA, leveraging Conformal Prediction (CP) to generate prediction sets that include the true label with a specified coverage probability.