Our experimental results demonstrate that CUFIT outperforms previous methods across various medical image benchmarks.

Thissimple change not only improves accuracy but also significantly improves the quality of the predictive uncertainty estimation of Mixup in most cases under various forms of covariate shifts and out-of-distribution detection experiments.

We introduce a regularity notion that generalizes the notion of concavity.

Neural Architecture Search (NAS) wasfirst proposed toachievestate-of-the-art performance through thediscoveryofnewarchitecture patterns, without human intervention.

Regularization terms, suchasL2 regularization leading toweightdecay,arecommonly employed during training to improve a model's generalization capabilities.