๐ค AI Summary
ๆฌๆ้ๅฏนๅ็ฑปๅจๅฉ็จ่ๅ็ธๅ
ณๆง่้่ฏๆญ็นๅพ็้ฎ้ข๏ผๆๅบ้่ฟ่ฐๆดไธๅ็ป้ด็พ็
ๆต่ก็็ๆ กๅๆฅ็ผ่งฃ่ฟไธ้ฎ้ข๏ผไฝฟ็จ็ผ็ ๅจๆ ๅ
ณ็ๆนๆณ่ฟ่กๅค็ใ
๐ Abstract
Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing mitigation strategies mostly aim to learn shortcut-invariant representations; their empirical success is limited and they cannot be applied to classifiers using frozen foundation model encoders. We propose to reframe shortcut learning as fundamentally a calibration problem: unconstrained learning implicitly calibrates each shortcut group to its training set disease prevalence, rendering the resulting classifier necessarily over-confident in one group and under-confident in the other. Building on this insight, we prevalence-equalize calibration between shortcut groups through two encoder-agnostic methods, an in-processing regularizer and a post-hoc prevalence-equalized recalibration step. Across chest-drain-pneumothorax benchmarks on CheXpert and SIIM-ACR, spanning fine-tuned CNNs and frozen foundation-model backbones, both methods substantially outperform all baselines. Post-hoc recalibration of a standard ERM-trained DenseNet raises misaligned-group AUROC from 0.23 to 0.73, indicating that shortcut reliance degrades the classification head rather than the underlying representation. Besides two new state-of-the-art shortcut mitigation approaches, our findings more fundamentally connect shortcut learning to calibration theory and algorithmic fairness.