  Maximum Probability Framework, powered by Maximum Probability Theorem, is a
recent theoretical development in artificial intelligence, aiming to formally
define probabilistic models, guiding development of objective functions, and
regularization of probabilistic models. MPT uses the probability distribution
that the models assume on random variables to provide an upper bound on the
probability of the model. We apply MPT to challenging out-of-distribution (OOD)
detection problems in computer vision by incorporating MPT as a regularization
scheme in the training of CNNs and their energy-based variants. We demonstrate
the effectiveness of the proposed method on 1080 trained models, with varying
hyperparameters, and conclude that the MPT-based regularization strategy
stabilizes and improves the generalization and robustness of base models in
addition to enhanced OOD performance on CIFAR10, CIFAR100, and MNIST datasets.
