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Transferable Calibration with Lower Bias and Variance in Domain Adaptation

NeurIPS 2020 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Source-free domain adaptation · 22 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Apply TransCal instead of temperature scalingCDAN · Office-Home (9 tasks; Avg)ECE (%): 16.8 -> 13.5Helped
Apply TransCal instead of IID temperature scalingMDD · Office-Home (6 tasks; Avg)ECE (%): 17.5 -> 11.6Helped
Apply TransCal instead of CPCSMCD · Office-Home (6 tasks; Avg)ECE (%): 18.4 -> 12.0Helped
Apply TransCal instead of CPCSMDD · ImageNet-Sketch (ImageNet→Sketch)ECE (%): 14.2 -> 8.1Helped
Apply TransCal instead of CPCSMCD · VisDA (Simulation→Real)ECE (%): 22.9 -> 7.8Helped
Apply TransCal instead of temperature scalingMulti-Domain Sentiment · 12 transfer tasks (Avg)ECE (%): 9.2 -> 5.9Helped
Add learnable bias-reduction term (ablation)TransCal with CDAN · Office-Home (9 tasks; Avg)ECE (%): 14.5 -> 13.5Helped
Add variance-reduction term (ablation)TransCal with CDAN · Office-Home (9 tasks; Avg)ECE (%): 18.6 -> 13.5Helped
Add control variate (ablation)TransCal with MDD · Office-Home A→CECE (%): 20.9±4.68 -> 13.5±3.51Helped
Use serial rather than parallel control variates (ablation)TransCal with MDD · Office-Home A→CECE (%): 13.6±4.43 -> 13.5±3.51No clear effect
Use serial rather than parallel control variates (ablation)TransCal with MDD · Office-Home A→PECE (%): 10.6±1.46 -> 11.4±0.81Mixed

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