SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 2 shows the risks for different approaches with  $\pi_Q = 0.1$ ,  $n_P = 10^3$ , and  $n_Q = 10^2$ . In terms of  $R^{\mathrm{cal}}$ , COMPOSITE performs the best, as it is calibrated to the target distribution by taking advantage of the abundant source data. In terms of  $R^{\mathrm{sha}}$ , LABEL-SHIFT achieves  $R^{\mathrm{sha}} = 0$  due to the strictly increasing  $\hat{g}$ , but it suffers from high  $R^{\mathrm{cal}}$ . COMPOSITE and SOURCE achieve smaller  $R^{\mathrm{sha}}$  than TARGET, as a result of using more bins on a larger sample. Considering the combined impact of calibration and sharpness, our approach COMPOSITE attains the lowest overall recalibration risk  $R$  as well as MSE.
Figure 6 shows the optimal recalibration function  $h^*$  and the recalibration functions for the four approaches. It can be seen that COMPOSITE best estimates  $h^*$  with the highest resolution.