arXiv AI By Jungwon Choi, Hyeonseo Jang, Kibok Lee, Eunwoo Kim

Rethinking the Test-Time Prompt Tuning Objective from the Perspective of Calibration

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arXiv Machine Learning
Aug 27

Sample Margin-Aware Recalibration of Temperature Scaling

The paper introduces SMART, a lightweight recalibration technique that adjusts logits based on the margin between the top two logits, called the logit gap. It uses a soft-binned Expected Calibration Error objective to balance bias and variance, enabling stable updates even with limited calibration data. Experiments across various datasets and architectures show SMART achieves state‑of‑the‑art calibration with fewer parameters than existing methods.

By Haolan Guo, Linwei Tao, Haoyang Luo, Minjing Dong, Chang Xu
Hugging Face Trending Papers
Jun 21

Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation

Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destabilize online updates. We study robust test-time adaptation (RTTA) in the adversarial-stream setting, which remains comparatively underexplored relative to standard TTA, and propose SAFER (Stochastic Augmentation Framework for Enhanced Robustness), a training-free reliability-guided augmentation wrapper for RTTA.