Title: Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks,
Abstract: Label shift refers to the phenomenon where the marginal probability p(y) of
observing a particular class changes between the training and test
distributions while the conditional probability p(x|y) stays fixed. This is
relevant in settings such as medical diagnosis, where a classifier trained to
predict disease based on observed symptoms may need to be adapted to a
different distribution where the baseline frequency of the disease is higher.
Given calibrated estimates of p(y|x), one can apply an EM algorithm to correct
for the shift in class imbalance between the training and test distributions
without ever needing to calculate p(x|y). Unfortunately, modern neural networks
typically fail to produce well-calibrated probabilities, compromising the
effectiveness of this approach. Although Temperature Scaling can greatly reduce
miscalibration in these networks, it can leave behind a systematic bias in the
probabilities that still poses a problem. To address this, we extend
Temperature Scaling with class-specific bias parameters, which largely
eliminates systematic bias in the calibrated probabilities and allows for
effective domain adaptation under label shift. We term our calibration approach
"Bias-Corrected Temperature Scaling". On experiments with CIFAR10, we find that
EM with Bias-Corrected Temperature Scaling significantly outperforms both EM
with Temperature Scaling and the recently-proposed Black-Box Shift Estimation.