Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
This work addresses OOD detection and model robustness for computer vision applications, representing an incremental improvement through a novel regularization approach.
The paper tackled out-of-distribution detection in computer vision by applying the Maximum Probability Theorem as a regularization scheme during CNN training, resulting in improved generalization, robustness, and OOD performance across CIFAR10, CIFAR100, and MNIST datasets, as demonstrated on 1080 trained models.
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.