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About this paper
Youjia Zhang, Huiling Liu, Soyun Choi, Jaehong Yoon, Sungeun Hong
GAIN uses compact statistics from unlabeled target data to propose sample-specific corrections to a frozen classifier’s predictions, then estimates whether each correction is preferable to keeping the source prediction. In ImageNet-C continual structured change, it reports 61.9% accuracy and 6.0% expected calibration error, versus 44.2% and 5.4% for the source; its optimum is limited to its evaluator and chosen path.
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