Accurate and Reliable Predictions with Mutual-Transport Ensemble

Table 3 presents the performance results for various models in detecting misclassifications. Our method showed significant improvements over other single-model calibration techniques and the DE method. OOD Detection: A reliable classification model should exhibit higher prediction uncertainty and lower confidence when encountering test samples significantly different from the training data. We assessed different calibration methods' abilities to differentiate OOD samples by blending indistribution test data with OOD data. We assessed two capabilities of models trained on CIFAR-10 and CIFAR-100: far OOD detection and near OOD detection Fort et al. (2019); Hendrycks et al. (2019). Far OOD detection involved distinguishing between CIFAR-10 and SVHN datasets Netzer et al. (2011) for models trained on CIFAR-10, and between CIFAR-100 and SVHN datasets for models trained on CIFAR-100. Near OOD detection required distinguishing between CIFAR-10 and CIFAR-100 datasets, which have similar domains. The results, presented in Table 4, demonstrate significant improvements of our method compared to other single-model calibration methods, even surpassing the performance of the DE method, known for its effectiveness in OOD detection.