Architectures with stronger inductive biases attain higher in-distribution accuracy but degrade faster under temporal distribution shift, while frozen pretrained encoders trade accuracy for stability.
Failing loudly: An empirical study of methods for detecting dataset shift
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Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift
Architectures with stronger inductive biases attain higher in-distribution accuracy but degrade faster under temporal distribution shift, while frozen pretrained encoders trade accuracy for stability.